Online Object Tracking: A Benchmark

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发布于 2022-05-09 07:59:50

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发布于 2022-05-09 07:59:50

文章被收录于专栏：SnailTyanSnailTyan

Online?Object?Tracking:?A?Benchmark

在线目标跟踪：基准

Abstract——摘要

Object?tracking?is?one?of?the?most?important?components?in?numerous?applications?of?computer?vision.?

目标跟踪是许多计算机应用中最重要的部分之一。

While?much?progress?has?been?made?in?recent?years?with?efforts?on?sharing?code?and?datasets,?it?is?of?great?importance?to?develop?a?library?and?benchmark?to?gauge?the?state?of?the?art.?

近些年通过努力在共享代码和数据集方面取得了许多进步，开发一个基准数据集的库来衡量目前的方法是非常重要。

After?briefly?reviewing?recent?advances?of?online?object?tracking,?we?carry?out?large?scale?experiments?with?various?evaluation?criteria?to?understand?how?these?algorithms?perform.

在简单回顾在线目标跟踪最近的发展之后，我们用不同的评价标准进行了大量的实验来了解这些算法的性能。

The?test?image?sequences?are?annotated?with?different?attributes?for?performance?evaluation?and?analysis.

为了进行性能评估和分析，我们对这些测试数据集用不同的属性进行了标注。

?By?analyzing?quantitative?results,?we?identify?effective?approaches?for?robust?tracking?and?provide?potential?future?research?directions?in?this?field.

通过分析大量的结果，我们识别了鲁棒跟踪的有效方法并在这个领域提供了将来潜在的研究方向。

1.?Introduction——引言

段落1——目标跟踪的定义和目标的进展

Object?tracking?is?one?of?the?most?important?components?in?a?wide?range?of?applications?in?computer?vision,?such?as?surveillance,?human?computer?interaction,?and?medical?imaging?[60,?12].

References——计算机视觉应用

[60]?A.?Yilmaz,?O.?Javed,?and?M.?Shah.?Object?Tracking:?A?Survey.?ACM?Computing?Surveys,?38(4):1–45,?2006.?

[12]?K.?Cannons.?A?Review?of?Visual?Tracking.?Technical?Report?CSE2008-07,?York?University,?Canada,?2008.?

目标跟踪计算机视觉广泛应用中最重要的部分之一，这些应用例如监控，人机交互，医疗图像。

Given?the?initialized?state?(e.g.,?position?and?size)?of?a?target?object?in?a?frame?of?a?video,?the?goal?of?tracking?is?to?estimate?the?states?of?the?target?in?the?subsequent?frames.

给定视频帧中目标对象的初始状态(例如：位置和尺寸)，跟踪的目标是在接下来的帧中评估目标的状态。

?Although?object?tracking?has?been?studied?for?several?decades,?and?much?progress?has?been?made?in?recent?years?[28,?16,?47,?5,?40,?26,?19],?it?remains?a?very?challenging?problem.

References——目标跟踪中的进步

[28]?M.?Isard?and?A.?Blake.?CONDENSATION–Conditional?Density?Propagation?for?Visual?Tracking.?IJCV,?29(1):5–28,?1998.?

[16]?D.?Comaniciu,?V.?Ramesh,?and?P.?Meer.?Kernel-Based?Object?Tracking.?PAMI,?25(5):564–577,?2003.?

[47]?D.?Ross,?J.?Lim,?R.-S.?Lin,?and?M.-H.?Yang.?Incremental?Learning?for?Robust?Visual?Tracking.?IJCV,?77(1):125–141,?2008.?

[5]?B.?Babenko,?M.-H.?Yang,?and?S.?Belongie.?Visual?Tracking?with?Online?Multiple?Instance?Learning.?In?CVPR,?2009.?

[40]?X.?Mei?and?H.?Ling.?Robust?Visual?Tracking?using?L1?Minimization.?In?ICCV,?2009.?

[26]?S.?Hare,?A.?Saffari,?and?P.?H.?S.?Torr.?Struck:?Structured?Output?Tracking?with?Kernels.?In?ICCV,?2011.?

[19]?J.?Fan,?X.?Shen,?and?Y.?Wu.?Scribble?Tracker:?A?Matting-based?Approach?for?Robust?Tracking.?PAMI,?34(8):1633–1644,?2012.?

尽管目标跟踪已经研究了好多年，在近些年取得了许多进步，但它仍是一个非常具有挑战性的问题。

?Numerous?factors?affect?the?performance?of?a?tracking?algorithm,?such?as?illumination?variation,?occlusion,?as?well?as?background?clutters,?and?there?exists?no?single?tracking?approach?that?can?successfully?handle?all?scenarios.

?许多因素会影响跟踪算法的性能，例如光照变化，遮挡，杂乱背景，没有一个跟踪方法能成功的处理所有场景。

Therefore,it?is?crucial?to?evaluate?the?performance?of?state-of-the-art?trackers?to?demonstrate?their?strength?and?weakness?and?help?identify?future?research?directions?in?this?field?for?designing?more?robust?algorithms.

因此，评估目标的算法的性能的关键是证明它们的长处和弱点并有助于这个领域将来的研究方向，从而设计更鲁棒的算法。

段落2——跟踪数据集的介绍

For?comprehensive?performance?evaluation,?it?is?critical?to?collect?a?representative?dataset.?

进行复杂的性能评估的关键是收集有代表性的数据集。

There?exist?several?datasets?for?visual?tracking?in?the?surveillance?scenarios,such?as?the?VIVID?[14],?CAVIAR?[21],?and?PETS?databases.

References——跟踪数据集

[14]?R.?Collins,?X.?Zhou,?and?S.?K.?Teh.?An?Open?Source?Tracking?Testbed?and?Evaluation?Web?Site.?In?PETS,?2005.?

[21]?R.?B.?Fisher.?The?PETS04?Surveillance?Ground-Truth?Data?Sets.?In?PETS,?2004.?

目标有一些监控视频中进行目标跟踪的数据集，例如VIVID,CAVIAR

和PETS数据集。

However,?the?target?objects?are?usually?humans?or?cars?of?small?size?in?these?surveillance?sequences,?and?the?background?is?usually?static.?

然而，在这些监控序列中目标对象通常是小尺寸的人或车，背景通常是静态的。

Although?some?tracking?datasets?[47,?5,?33]?for?generic?scenes?are?annotated?with?bounding?box,?most?of?them?are?not.?

References——跟踪数据集

[47]?D.?Ross,?J.?Lim,?R.-S.?Lin,?and?M.-H.?Yang.?Incremental?Learning?for?Robust?Visual?Tracking.?IJCV,?77(1):125–141,?2008.?

[5]?B.?Babenko,?M.-H.?Yang,?and?S.?Belongie.?Visual?Tracking?with?Online?Multiple?Instance?Learning.?In?CVPR,?2009.?

[33]?J.?Kwon?and?K.?M.?Lee.?Visual?Tracking?Decomposition.?In?CVPR,?2010.?

尽管一些一般场景的跟踪数据集标注了边界框，但大部分没有。

For?sequences?without?labeled?ground?truth,?it?is?difficult?to?evaluate?tracking?algorithms?as?the?reported?results?are?based?on?inconsistently?annotated?object?locations.

对于没标注真实目标的序列，由于发表的结果是基于不同的标注位置的，因此评估跟踪算法是很困难的。

段落3——跟踪数据集和跟踪算法的介绍

Recently,?more?tracking?source?codes?have?been?made?publicly?available,?e.g.,?the?OAB?[22],?IVT?[47],?MIL?[5],?L1?[40],?and?TLD?[31]?algorithms,?which?have?been?commonly?used?for?evaluation.?

References——可获得源码的跟踪算法

[22]?H.?Grabner,?M.?Grabner,?and?H.?Bischof.?Real-Time?Tracking?via?On-line?Boosting.?In?BMVC,?2006.?

[47]?D.?Ross,?J.?Lim,?R.-S.?Lin,?and?M.-H.?Yang.?Incremental?Learning?for?Robust?Visual?Tracking.?IJCV,?77(1):125–141,?2008.?

[5]?B.?Babenko,?M.-H.?Yang,?and?S.?Belongie.?Visual?Tracking?with?Online?Multiple?Instance?Learning.?In?CVPR,?2009.?

[40]?X.?Mei?and?H.?Ling.?Robust?Visual?Tracking?using?L1?Minimization.?In?ICCV,?2009.?

[31]?Z.?Kalal,?J.?Matas,?and?K.?Mikolajczyk.?P-N?Learning:?Bootstrapping?inary?Classifiers?by?Structural?Constraints.?In?CVPR,?2010.?

最近，更多的跟踪源码是可公开获得的，例如，OAB，IVT，MIL，L1,TLD算法，这些通常是对评估有用的。

However,?the?input?and?output?formats?of?most?trackers?are?different?and?thus?it?is?inconvenient?for?large?scale?performance?evaluation.?

然而，大部分跟踪器的输入输出格式是不同的，因此进行大规模性能评估是不便宜的。

In?this?work,we?build?a?code?library?that?includes?most?publicly?available?trackers?and?a?test?dataset?with?ground-truth?annotations?to?facilitate?the?evaluation?task.

在这个工作，我们建立一个包括大多数公开可获得的跟踪器的代码库和有利于评估工作的实际标注的测试数据集。

?Additionally?each?sequence?in?the?dataset?is?annotated?with?attributes?that?often?affect?tracking?performance,?such?as?occlusion,?fast?motion,?and?illumination?variation.

此外，每个数据集序列标注了经常影响跟踪性能的属性，例如遮挡，快速运动和光照变化。

段落4——算法性能评估的标准

One?common?issue?in?assessing?tracking?algorithms?is?that?the?results?are?reported?based?on?just?a?few?sequences?with?different?initial?conditions?or?parameters.?

在估计跟踪算法时一个共同的问题是发表的结果是基于一些用不同的初始化环境或参数的序列的。

Thus,?the?results?do?not?provide?the?holistic?view?of?these?algorithms.

因此，这些结果没有提供这些算法的整体轮廓。

For?fair?and?comprehensive?performance?evaluation,?we?propose?to?perturb?the?initial?state?spatially?and?temporally?from?the?ground-truth?target?locations.?

为了公平和复杂的性能评估，我们计划从空间和时间上扰乱实际目标位置的初始化状态。

While?the?robustness?to?initialization?is?a?well-known?problem?in?the?field,?it?is?seldom?addressed?in?the?literature.?

虽然初始化的鲁棒性在这个领域是一个非常有名问题，但在文章中很少讲述。

To?the?best?of?our?knowledge,?this?is?the?first?comprehensive?work?to?address?and?analyze?the?initialization?problem?of?object?tracking.

我们所知，这是第一个全面讲述和分析目标跟踪初始化问题的工作。

We?use?the?precision?plots?based?on?location?error?metric?and?the?success?plots?based?on?the?overlap?metric,?to?analyze?the?performance?of?each?algorithm.

我们使用用基于位置错误度量的准确率图和基于重叠率度量的成功图来分析每一个算法的性能。

段落5——本文的贡献

The?contribution?of?this?work?is?three-fold:

本文贡献有三个方面：

Dataset.?We?build?a?tracking?dataset?with?50?fully?annotated?sequences?to?facilitate?tracking?evaluation.

数据集。为了方便跟踪评估我们建立一个跟踪数据集，包括五十个全面标注的序列。

Code?library.?We?integrate?most?publicly?available?trackers?in?our?code?library?with?uniform?input?and?output?formats?to?facilitate?large?scale?performance?evaluation.?At?present,it?includes?29?tracking?algorithms.

代码库。我们在代码库中集成了大多数公开可获得的跟踪器用统一的输入输出格式，这有利于大规模的性能评估。目前，它包括29个跟踪算法。

Robustness?evaluation.?The?initial?bounding?boxes?for?tracking?are?sampled?spatially?and?temporally?to?evaluate?the?robustness?and?characteristics?of?trackers.?Each?tracker?is?extensively?evaluated?by?analyzing?more?than?660,000?bounding?box?outputs.

鲁棒性评估。为了评估跟踪器的鲁棒性和特性，跟踪的初始化边界框是由时间和空间上采样得到的。每个跟踪器是通过分析超过660000个边界框输出来广泛评估的。

段落5——本文的贡献

This?work?mainly?focuses?on?the?online?tracking?of?single?target.?The?code?library,?annotated?dataset?and?all?the?tracking?results?are?available?on?the?website?http://visualtracking.net?.

这个工作主要集中在单目标的在线跟踪上。这个代码库，标注的数据集和所有跟踪结果能在网站http://visualtracking.net上得到。

2.?Related?Work

In?this?section,?we?review?recent?algorithms?for?object?tracking?in?terms?of?several?main?modules:?target?representation?scheme,?search?mechanism,?and?model?update.?

在这一节，我们按照一些主要模块回顾了目标跟踪最近的一些算法：目标表示方案，搜索机制和模型更新。

In?addition,?some?methods?have?been?proposed?that?build?on?combing?some?trackers?or?mining?context?information.?

此外，已经提出了一些建立在结合一些跟踪器上和挖掘上下文信息的的方法。

Representation?Scheme.?Object?representation?is?one?of?major?components?in?any?visual?tracker?and?numerous?schemes?have?been?presented?[35].?

References——目标表示方案

[35]?X.?Li,?W.?Hu,?C.?Shen,?Z.?Zhang,?A.?Dick,?and?A.?Hengel.?A?Survey?of?Appearance?Models?in?Visual?Object?Tracking.?TIST,?2013,?in?press.?

表示方案。目标表示是在任何视觉跟踪器中的一个主要部分并且已经提出了许多的方案。

Since?the?pioneering?work?of?Lucas?and?Kanade?[37,?8],?holistic?templates?(raw?intensity?values)?have?been?widely?used?for?tracking?[25,?39,?2].?

References——整体模版的开创性工作

[37]?B.?D.?Lucas?and?T.?Kanade.?An?Iterative?Image?Registration?Technique?with?An?Application?to?Stereo?Vision.?In?IJCAI,?1981.?

[8]?S.?Baker?and?I.?Matthews.?Lucas-Kanade?20?Years?On:?A?Unifying?Framework.?IJCV,?56(3):221–255,?2004.?

References——整体模版

[25]?G.?D.?Hager?and?P.?N.?Belhumeur.?Efficient?Region?Tracking?With?Parametric?Models?of?Geometry?and?Illumination.?PAMI,?20(10):1025–1039,?1998.?

[39]?I.?Matthews,?T.?Ishikawa,?and?S.?Baker.?The?Template?Update?Problem.?PAMI,?26(6):810–815,?2004.?

[2]?N.?Alt,?S.?Hinterstoisser,?and?N.?Navab.?Rapid?Selection?of?Reliable?Templates?for?Visual?Tracking.?In?CVPR,?2010.?

由于Lucas和Kanade的开拓性工作，整体模版(原始强度值)已经在跟踪中得到了广泛应用。

Subsequently,?subspace-based?tracking?approaches?[11,?47]?have?been?proposed?to?better?account?for?appearance?changes.?

References——基于子空间的跟踪方法

[11]?M.?J.?Black.?EigenTracking:?Robust?Matching?and?Tracking?of?Articulated?Objects?Using?a?View-Based?Representation.?IJCV,?26(1):63–?84,?1998.?

[47]?D.?Ross,?J.?Lim,?R.-S.?Lin,?and?M.-H.?Yang.?Incremental?Learning?for?Robust?Visual?Tracking.?IJCV,?77(1):125–141,?2008.?

接下来，提出的基于子空间的跟踪方法更好的考虑了外观变化。

Furthermore,?Mei?and?Ling?[40]?proposed?a?tracking?approach?based?on?sparse?representation?to?handle?the?corrupted?appearance?and?recently?it?has?been?further?improved?[41,?57,?64,?10,?55,?42].?

References——基于稀疏表示的跟踪

[40]?X.?Mei?and?H.?Ling.?Robust?Visual?Tracking?using?L1?Minimization.?In?ICCV,?2009.?

[41]?X.?Mei,?H.?Ling,?Y.?Wu,?E.?Blasch,?and?L.?Bai.?Minimum?Error?Bounded?Efficient? L1?Tracker?with?Occlusion?Detection.?In?CVPR,?2011.?

[57]?Y.?Wu,?H.?Ling,?J.?Yu,?F.?Li,?X.?Mei,?and?E.?Cheng.?Blurred?Target?Tracking?by?Blur-driven?Tracker.?In?ICCV,?2011.?

[64]?T.?Zhang,?B.?Ghanem,?S.?Liu,?and?N.?Ahuja.?Robust?Visual?Tracking?via?Multi-task?Sparse?Learning.?In?CVPR,?2012.?

[10]?C.?Bao,?Y.?Wu,?H.?Ling,?and?H.?Ji.?Real?Time?Robust?L1?Tracker?Using?Accelerated?Proximal?Gradient?Approach.?In?CVPR,?2012.?

[55]?D.?Wang,?H.?Lu,?and?M.-H.?Yang.?Online?Object?Tracking?with?Sparse?Prototypes.?TIP,?22(1):314–325,?2013.?

[42]?X.?Mei,?H.?Ling,?Y.?Wu,?E.?Blasch,?and?L.?Bai.?Efficient?Minimum?Error?Bounded?Particle?Resampling?L1?Tracker?with?Occlusion?Detection.?TIP,?2013,?in?press.?

此外，Mei和Ling提出了基于稀疏表示的跟踪方法来处理毁坏的外观，最近它已经被进一步改进了。

In?addition?to?template,?many?other?visual?features?have?been?adopted?in?tracking?algorithms,?such?as?color?histograms?[16],?histograms?of?oriented?gradients?(HOG)?[17,?52],?covariance?region?descriptor?[53,?46,?56]?and?Haar-like?features?[54,?22].?

References——跟踪中使用的视觉特征

[16]?D.?Comaniciu,?V.?Ramesh,?and?P.?Meer.?Kernel-Based?Object?Tracking.?PAMI,?25(5):564–577,?2003.?

[17]?N.?Dalal?and?B.?Triggs.?Histograms?of?Oriented?Gradients?for?Human?Detection.?In?CVPR,?2005.?

[52]?F.?Tang,?S.?Brennan,?Q.?Zhao,?and?H.?Tao.?Co-Tracking?Using?Semi-Supervised?Support?Vector?Machines.?CVPR,?2007.

[53]?O.?Tuzel,?F.?Porikli,?and?P.?Meer.?Region?Covariance:?A?Fast?Descriptor?for?Detection?and?Classification.?In?ECCV,?2006.?

[46]?F.?Porikli,?O.?Tuzel,?and?P.?Meer.?Covariance?Tracking?using?Model?Update?based?on?Lie?Algebra.?In?CVPR,?2006.

[56]?Y.?Wu,?J.?Cheng,?J.?Wang,?H.?Lu,?J.?Wang,?H.?Ling,?E.?Blasch,?and?L.?Bai.?Real-time?Probabilistic?Covariance?Tracking?with?Efficient?Model?Update.?TIP,?21(5):2824–2837,?2012.?

[54]?P.?Viola?and?M.?J.?Jones.?Robust?Real-Time?Face?Detection.?IJCV,?57(2):137–154,?2004.?

[22]?H.?Grabner,?M.?Grabner,?and?H.?Bischof.?Real-Time?Tracking?via?On-line?Boosting.?In?BMVC,?2006.

除了模版之外，在跟踪方法中使用了许多其它的视觉特征，例如颜色直方图，梯度直方图，协方差区域描述子和Haar-like特征。

Recently,?the?discriminative?model?has?been?widely?adopted?in?tracking?[15,?4],?where?a?binary?classifier?is?learned?online?to?discriminate?the?target?from?the?background.?

References——判别式模型

[15]?R.?T.?Collins,?Y.?Liu,?and?M.?Leordeanu.?Online?Selection?of?Discriminative?Tracking?Features.?PAMI,?27(10):1631–1643,?2005.?

[4]?S.?Avidan.?Ensemble?Tracking.?PAMI,?29(2):261–271,?2008.?

最近，判别式模型在跟踪中得到了广泛采纳，在线学习一个二值分类器来从背景中区分目标。

Numerous?learning?methods?have?been?adapted?to?the?tracking?problem,?such?as?SVM?[3],?structured?output?SVM?[26],?ranking?SVM?[7],?boosting?[4,?22],?semi-boosting?[23]?and?multi-instance?boosting?[5].

References——跟踪中的学习方法

[3]?S.?Avidan.?Support?Vector?Tracking.?PAMI,?26(8):1064–1072,?2004.?

[26]?S.?Hare,?A.?Saffari,?and?P.?H.?S.?Torr.?Struck:?Structured?Output?Tracking?with?Kernels.?In?ICCV,?2011.?

[7]?Y.?Bai?and?M.?Tang.?Robust?Tracking?via?Weakly?Supervised?Ranking?SVM.?In?CVPR,?2012.?

[4]?S.?Avidan.?Ensemble?Tracking.?PAMI,?29(2):261–271,?2008.?

[22]?H.?Grabner,?M.?Grabner,?and?H.?Bischof.?Real-Time?Tracking?via?On-line?Boosting.?In?BMVC,?2006.?

[23]?H.?Grabner,?C.?Leistner,?and?H.?Bischof.?Semi-supervised?On-Line?Boosting?for?Robust?Tracking.?In?ECCV,?2008.?

[5]?B.?Babenko,?M.-H.?Yang,?and?S.?Belongie.?Visual?Tracking?with?Online?Multiple?Instance?Learning.?In?CVPR,?2009.?

许多学习方法已经在跟踪问题中得到采纳，例如SVM，结构化SVM，排序SVM，boosting，semi-boosting，多实例boosting。

?To?make?trackers?more?robust?to?pose?variation?and?partial?occlusion,?an?object?can?be?represented?by?parts?where?each?one?is?represented?by?descriptors?or?histograms.?

为了使跟踪器对姿态变化和部分遮挡更鲁棒，一个目标可以由许多部分表示，每一部分可以由描述子或直方图表示。

In?[1]?several?local?histograms?are?used?to?represent?the?object?in?a?pre-defined?grid?structure.

References——局部直方图

[1]?A.?Adam,?E.?Rivlin,?and?I.?Shimshoni.?Robust?Fragments-based?Tracking?using?the?Integral?Histogram.?In?CVPR,?2006.?

在1中一些局部直方图用来表示对象在预定义的格子结构。

Kwon?and?Lee?[32]?propose?an?approach?to?automatically?update?the?topology?of?local?patches?to?handle?large?pose?changes.

References——自动更新局部块的拓扑结构

[32]?J.?Kwon?and?K.?M.?Lee.?Tracking?of?a?Non-Rigid?Object?via?Patch-based?Dynamic?Appearance?Modeling?and?Adaptive?Basin?Hopping?Monte?Carlo?Sampling.?In?CVPR,?2009.?

Kwon和Lee?提出了一种自动更新局部块拓扑结构的方法来处理大的姿态变化。

?To?better?handle?appearance?variations,?some?approaches?regarding?integration?of?multiple?representation?schemes?have?recently?been?proposed?[62,?51,?33].?

References——一些表示方案结合

[62]?L.?Yuan,?A.?Haizhou,?T.?Yamashita,?L.?Shihong,?and?M.?Kawade.?Tracking?in?Low?Frame?Rate?Video:?A?Cascade?Particle?Filter?with?Discriminative?Observers?of?Different?Life?Spans.?PAMI,?30(10):1728–1740,?2008.?

[51]?B.?Stenger,?T.?Woodley,?and?R.?Cipolla.?Learning?to?Track?with?Multiple?Observers.?In?CVPR,?2009.?

[33]?J.?Kwon?and?K.?M.?Lee.?Visual?Tracking?Decomposition.?In?CVPR,?2010.?

为了更好的处理外观变化，最近提出了一些方法把多个表示方案结合起来。

Search?Mechanism.?To?estimate?the?state?of?the?target?objects,?deterministic?or?stochastic?methods?have?been?used.?

搜索方案。为了估计目标对象的状态，已经提出了一些确定性和随机的方法。

When?the?tracking?problem?is?posed?within?an?optimization?framework,?assuming?the?objective?function?is?differentiable?with?respect?to?the?motion?parameters,?gradient?descent?methods?can?be?used?to?locate?the?target?efficiently?[37,?16,?20,?49].?

References——梯度下降法定位目标

[37]?B.?D.?Lucas?and?T.?Kanade.?An?Iterative?Image?Registration?Technique?with?An?Application?to?Stereo?Vision.?In?IJCAI,?1981.?

[16]?D.?Comaniciu,?V.?Ramesh,?and?P.?Meer.?Kernel-Based?Object?Tracking.?PAMI,?25(5):564–577,?2003.?

[20]?J.?Fan,?Y.?Wu,?and?S.?Dai.?Discriminative?Spatial?Attention?for?Robust?Tracking.?In?ECCV,?2010.?

[49]?L.?Sevilla-Lara?and?E.?Learned-Miller.?Distribution?Fields?for?Tracking.?In?CVPR,?2012.?

把跟踪问题放在一个优化框架中时，假设目标函数是关于运动参数可区分的，梯度下降法能用来有效定位目标。

However,?these?objective?functions?are?usually?nonlinear?and?contain?many?local?minima.?

然而，这些目标函数通常是非线性的且包含许多局部最小值。

To?alleviate?this?problem,?dense?sampling?methods?have?been?adopted?[22,?5,?26]?at?the?expense?of?high?computational?load.?

为了减少这个问题，采用了高计算负载的密集采样方法。

On?the?other?hand,?stochastic?search?algorithms?such?as?particle?filters?[28,?44]?have?been?widely?used?since?they?are?relatively?insensitive?to?local?minima?and?computationally?efficient?[47,?40,?30].?

References——随机搜索算法

[28]?M.?Isard?and?A.?Blake.?CONDENSATION–Conditional?Density?Propagation?for?Visual?Tracking.?IJCV,?29(1):5–28,?1998.?

[44]?P.?P′erez,?C.?Hue,?J.?Vermaak,?and?M.?Gangnet.?Color-Based?Probabilistic?Tracking.?In?ECCV,?2002.?

[47]?D.?Ross,?J.?Lim,?R.-S.?Lin,?and?M.-H.?Yang.?Incremental?Learning?for?Robust?Visual?Tracking.?IJCV,?77(1):125–141,?2008.?

[40]?X.?Mei?and?H.?Ling.?Robust?Visual?Tracking?using?L1?Minimization.?In?ICCV,?2009.?

[30]?X.?Jia,?H.?Lu,?and?M.-H.?Yang.?Visual?Tracking?via?Adaptive?Structural?Local?Sparse?Appearance?Model.?In?CVPR,?2012.?

另一方面，随机搜索算法例如粒子滤波已经得到了广泛应用，由于他们对局部最小值相对敏感并且计算有效。

Model?Update.?It?is?crucial?to?update?the?target?representation?or?model?to?account?for?appearance?variations.?

模型更新。考虑到外观变化，更新目标表示或模型是非常关键的。

Matthews?et?al.?[39]?address?the?template?update?problem?for?the?Lucas-Kanade?algorithm?[37]?where?the?template?is?updated?with?the?combination?of?the?fixed?reference?template?extracted?from?the?first?frame?and?the?result?from?the?most?recent?frame.?

References——模型更新

[39]?I.?Matthews,?T.?Ishikawa,?and?S.?Baker.?The?Template?Update?Problem.?PAMI,?26(6):810–815,?2004.?

[37]?B.?D.?Lucas?and?T.?Kanade.?An?Iterative?Image?Registration?Technique?with?An?Application?to?Stereo?Vision.?In?IJCAI,?1981.?

Matthews等人讲述了Lucas-Kanade算法的模版更新问题，模版更新结合了第一帧提取的固定参考模版和最近帧的跟踪结果。

Effective?update?algorithms?have?also?been?proposed?via?online?mixture?model?[29],?online?boosting?[22],?and?incremental?subspace?update?[47].?

References——有效的模版更新算法

[29]?A.?D.?Jepson,?D.?J.?Fleet,?and?T.?F.?El-Maraghi.?Robust?Online?Appearance?Models?for?Visual?Tracking.?PAMI,?25(10):1296–1311,?2003.?

[22]?H.?Grabner,?M.?Grabner,?and?H.?Bischof.?Real-Time?Tracking?via?On-line?Boosting.?In?BMVC,?2006.?

[47]?D.?Ross,?J.?Lim,?R.-S.?Lin,?and?M.-H.?Yang.?Incremental?Learning?for?Robust?Visual?Tracking.?IJCV,?77(1):125–141,?2008.?

通过在线混合模型，在线boosting和增量子空间更新，提出了许多有效的更新算法。

For?discriminative?models,?the?main?issue?has?been?improving?the?sample?collection?part?to?make?the?online-trained?classifier?more?robust?[23,?5,?31,?26].?

References——判别式模型

[23]?H.?Grabner,?C.?Leistner,?and?H.?Bischof.?Semi-supervised?On-Line?Boosting?for?Robust?Tracking.?In?ECCV,?2008.?

[5]?B.?Babenko,?M.-H.?Yang,?and?S.?Belongie.?Visual?Tracking?with?Online?Multiple?Instance?Learning.?In?CVPR,?2009.?

[31]?Z.?Kalal,?J.?Matas,?and?K.?Mikolajczyk.?P-N?Learning:?Bootstrapping?inary?Classifiers?by?Structural?Constraints.?In?CVPR,?2010.?

[26]?S.?Hare,?A.?Saffari,?and?P.?H.?S.?Torr.?Struck:?Structured?Output?Tracking?with?Kernels.?In?ICCV,?2011.?

对于判别式模型，主要问题是改善采样收集部分从而使在线训练的分类器更鲁棒。

While?much?progress?has?been?made,?it?is?still?difficult?to?get?an?adaptive?appearance?model?to?avoid?drifts.?

虽然取得了很多进步，但是得到一个有效的避免漂移自适应外观模型仍然是困难的。

Context?and?Fusion?of?Trackers.?Context?information?is?also?very?important?for?tracking.?

上下文和跟踪器融合。对于跟踪上下文信息也是非常重要的。

Recently?some?approaches?have?been?proposed?by?mining?auxiliary?objects?or?local?visual?information?surrounding?the?target?to?assist?tracking?[59,?24,?18].?

References——辅助信息帮助跟踪

[59]?M.?Yang,?Y.?Wu,?and?G.?Hua.?Context-Aware?Visual?Tracking.?PAMI,?31(7):1195–1209,?2008.?

[24]?H.?Grabner,?J.?Matas,?L.?V.?Gool,?and?P.?Cattin.?Tracking?the?Invisible:?Learning?Where?the?Object?Might?be.?In?CVPR,?2010.?

[18]?T.?B.?Dinh,?N.?Vo,?and?G.?Medioni.?Context?Tracker:?Exploring?Supporters?and?Distracters?in?Unconstrained?Environments.?In?CVPR,?2011.?

最近已经提出了一些通过挖掘辅助对象或在目标周围的一些局部视觉信息的方法来辅助跟踪。

The?context?information?is?especially?helpful?when?the?target?is?fully?occluded?or?leaves?the?image?region?[24].?

References——上下文信息

[24]?H.?Grabner,?J.?Matas,?L.?V.?Gool,?and?P.?Cattin.?Tracking?the?Invisible:?Learning?Where?the?Object?Might?be.?In?CVPR,?2010.?

当目标是全部被遮挡或离开图像区域时，上下文信息是尤其有帮助的。

To?improve?the?tracking?performance,?some?tracker?fusion?methods?have?been?proposed?recently.?

为了提高跟踪性能，最近提出了一些跟踪器融合的方法。

Santner?et?al.?[48]?proposed?an?approach?that?combines?static,?moderately?adaptive?and?highly?adaptive?trackers?to?account?for?appearance?changes.?

References——多种外观模型结合

[48]?J.?Santner,?C.?Leistner,?A.?Saffari,?T.?Pock,?and?H.?Bischof.?PROST:?Parallel?Robust?Online?Simple?Tracking.?In?CVPR,?2010.?

考虑到外观变化，Santner等人提出了一个结合静态、适度的适应和高适应跟踪器。

Even?multiple?trackers?[34]?or?multiple?feature?sets?[61]?are?maintained?and?selected?in?a?Bayesian?framework?to?better?account?for?appearance?changes.

References——多跟踪器，多特征

[34]?J.?Kwon?and?K.?M.?Lee.?Tracking?by?Sampling?Trackers.?In?ICCV,?2011.?

[61]?J.?H.?Yoon,?D.?Y.?Kim,?and?K.-J.?Yoon.?Visual?Tracking?via?Adaptive?Tracker?Selection?with?Multiple?Features.?In?ECCV,?2012.?

在贝叶斯框架中，为了更好的解释外观变化，甚至多跟踪器或多特征集被保留和选择。

3.?Evaluated?Algorithms?and?Datasets

段落

For?fair?evaluation,?we?test?the?tracking?algorithms?whose?original?source?or?binary?codes?are?publicly?available?as?all?implementations?inevitably?involve?technical?details?and?specific?parameter?settings.?

为了公平评估的，我们测试的跟踪算法的源码是公开可获得的，因此不可避免的包含技术细节和特定的参数设置。

备注：[36,58]是联系得到的，[44,16]是自己实现的。

Table?1?shows?the?list?of?the?evaluated?tracking?algorithms.

表一是评估的跟踪算法。

Table?1.?Evaluated?tracking?algorithms?(MU:?model?update,?FPS:frames?per?second).?For?representation?schemes,?L:?local,?H:holistic,?T:?template,?IH:?intensity?histogram,?BP:?binary?pattern,PCA:?principal?component?analysis,?SPCA:?sparse?PCA,?SR:?sparserepresentation,?DM:?discriminative?model,?GM:?generativemodel.?For?search?mechanism,?PF:?particle?filter,?MCMC:?Markov?Chain?Monte?Carlo,?LOS:?local?optimum?search,?DS:?dense?samplingsearch.?For?the?model?update,?N:?No,?Y:?Yes.?In?the?Code?column,?M:?Matlab,?C:C/C++,?MC:?Mixture?of?Matlab?and?C/C++,suffix?E:?executable?binary?code.

表一1、评估算法(MU：模型更新，FPS：每秒多少帧)。对于表示方案，L：局部的，H：整体的，T：模版，IH：强度直方图，BP：二值模式，PCA：主成分分析，SPCA：稀疏主成份分析，SR：稀疏表示，DM：判别式模型，GM：生成式模型。对于搜索机制：PF:粒子滤波，

MCMC：马尔科夫蒙特卡罗，LOS：局部最优搜索，DS：密集采样搜索。对于模型更新：N：不更新，Y：更新。在代码栏：M：Matlab，C：C/C++，MC：Matlab混合C/C++，suffix?E:可执行的二进制代码。

We?also?evaluate?the?trackers?in?the?VIVID?testbed?[14]?including?the?mean?shift?(MS-V),?template?matching?(TM-V),?ratio?shift?(RS-V)?and?peak?difference?(PD-V)?methods.?

References——VIVID试验台

[14]?R.?Collins,?X.?Zhou,?and?S.?K.?Teh.?An?Open?Source?Tracking?Testbed?and?Evaluation?Web?Site.?In?PETS,?2005.?

我们也评估了在VIVID试验台上的跟踪器，包括均值漂移，模版匹配，系数漂移，和峰差法。

段落

In?recent?years,?many?benchmark?datasets?have?been?developed?for?various?vision?problems,?such?as?the?Berkeley?segmentation?[38],?FERET?face?recognition?[45]?and?optical?flow?dataset?[9].?

References——各种数据集

[38]?D.?R.?Martin,?C.?C.?Fowlkes,?and?J.?Malik.?Learning?to?Detect?Natural?Image?Boundaries?Using?Local?Brightness,?Color,?and?Texture?Cues.?PAMI,?26(5):530–49,?2004.?

[45]?P.?Phillips,?H.?Moon,?S.?Rizvi,?and?P.?Rauss.?The?FERET?Evaluation?Methodology?for?Face-Recognition?Algorithms.?PAMI,?22(10):1090–1104,?2000.?

[9]?S.?Baker,?S.?Roth,?D.?Scharstein,?M.?J.?Black,?J.?Lewis,?and?R.?Szeliski.?A?Database?and?Evaluation?Methodology?for?Optical?Flow.?In?ICCV,?2007.?

在近些年，在各种视觉问题上已经开发了许多基本数据集，例如伯克利分割，FERET人脸识别和光流数据集。

There?exist?some?datasets?for?the?tracking?in?the?surveillance?scenario,?such?as?the?VIVID?[14]?and?CAVIAR?[21]?datasets.?

References——跟踪数据集

[14]?R.?Collins,?X.?Zhou,?and?S.?K.?Teh.?An?Open?Source?Tracking?Testbed?and?Evaluation?Web?Site.?In?PETS,?2005.?

[21]?R.?B.?Fisher.?The?PETS04?Surveillance?Ground-Truth?Data?Sets.?In?PETS,?2004.?

目前存在一些用来跟踪的监控场景，例如VIVID和CAVIAR数据集。

For?generic?visual?tracking,?more?sequences?have?been?used?for?evaluation?[47,?5].?

References——更多的评估序列

[47]?D.?Ross,?J.?Lim,?R.-S.?Lin,?and?M.-H.?Yang.?Incremental?Learning?for?Robust?Visual?Tracking.?IJCV,?77(1):125–141,?2008.?

[5]?B.?Babenko,?M.-H.?Yang,?and?S.?Belongie.?Visual?Tracking?with?Online?Multiple?Instance?Learning.?In?CVPR,?2009.?

对于一般的视觉跟踪，已经有更多的评估序列。

However,?most?sequences?do?not?have?the?ground?truth?annotations,?and?the?quantitative?evaluation?results?may?be?generated?with?different?initial?conditions.?

然而，大多数序列没有真实的标注，许多评估结果的可能有不同的初始化环境。

To?facilitate?fair?performance?evaluation,?we?have?collected?and?annotated?most?commonly?used?tracking?sequences.?

为了促进公平的性能评估，我们已经收集并标注了大多数的常用的跟踪序列。

Figure?1?shows?the?first?frame?of?each?sequence?where?the?target?object?is?initialized?with?a?bounding?box.?

图1是每个序列的第一帧，其中的目标有一个初始化的边框。

Figure?1.?Tracking?sequences?for?evaluation.?The?first?frame?with?the?bounding?box?of?the?target?object?is?shown?for?each?sequence.?The?sequences?are?ordered?based?on?our?ranking?results?(See?supplementary?material):?the?ones?on?the?top?left?are?more?difficult?for?tracking?than?the?ones?on?the?bottom?right.?Note?that?we?annotated?two?targets?for?the?jogging?sequence.

图1?评估的跟踪序列，第一帧中画出了目标的边框。这些序列被根据我们的排序结果进行了排序。左上角比右下角的更难跟踪。我们对慢跑序列标记了两个目标。

段落

Attributes?of?a?test?sequence.?Evaluating?trackers?is?difficult?because?many?factors?can?affect?the?tracking?performance.

测试序列的属性。由于有很多因素能影响跟踪性能，因此评估跟踪器是很困难的。

?For?better?evaluation?and?analysis?of?the?strength?and?weakness?of?tracking?approaches,?we?propose?to?categorize?the?sequences?by?annotating?them?with?the?11?attributes?shown?in?Table?2.?

为了更好的评估和分析跟踪方法的长处和弱点，我们计划根据表2标注的11属性对序列进行分类。

表2?测试序列中标注的属性列表，本文中用的阈值也显示出来了。

段落

The?attribute?distribution?in?our?dataset?is?shown?in?Figure?2(a).?Some?attributes?occur?more?frequently,?e.g.,?OPR?and?IPR,?than?others.?

数据集中的属性分布如图2所示，一些属性发生更频繁，例如

OPR和IPR比其它的更经常发生。

It?also?shows?that?one?sequence?is?often?annotated?with?several?attributes.

它也表明一个序列经常标注多个属性。?

Aside?from?summarizing?the?performance?on?the?whole?dataset,?we?also?construct?several?subsets?corresponding?to?attributes?to?report?specific?challenging?conditions.?

除了归纳整体数据集的性能之外，我们也构建了一些对应这些属性的子集来描述特定的有挑战性的环境。

For?example,?the?OCC?subset?contains?29?sequences?which?can?be?used?to?analyze?the?performance?of?trackers?to?handle?occlusion.?

例如，OCC子集包括29个序列可以用来分析跟踪器处理遮挡的性能。

The?attribute?distributions?in?OCC?subset?is?shown?in?Figure?2(b)?and?others?are?available?in?the?supplemental?material.

在OCC子集中的特性分布如图2(b)所示，其它的可在补充材料中得到。

4.?Evaluation?Methodology

段落

In?this?work,?we?use?the?precision?and?success?rate?for?quantitative?analysis.?In?addition,?we?evaluate?the?robustness?of?tracking?algorithms?in?two?aspects.?

在本文中，我们用准确率和成功率进行大量分析。此外，我们从两个方面评估跟踪算法的鲁棒性。

段落

Precision?plot.?One?widely?used?evaluation?metric?on?tracking?precision?is?the?center?location?error,?which?is?defined?as?the?average?Euclidean?distance?between?the?center?locations?of?the?tracked?targets?and?the?manually?labeled?ground?truths.?

精度图。一个得到广泛应用的评估跟踪精确度的度量标准是中心位置误差，中心位置误差是跟踪目标的中心位置与标注的中心位置的欧式距离。

Then?the?average?center?location?error?over?all?the?frames?of?one?sequence?is?used?to?summarize?the?overall?performance?for?that?sequence.?

图像序列所有帧的平均中心误差用来概括整个序列的整体性能。

However,?when?the?tracker?loses?the?target,?the?output?location?can?be?random?and?the?average?error?value?may?not?measure?the?tracking?performance?correctly?[6].?

References——平均中心误差

[6]?B.?Babenko,?M.-H.?Yang,?and?S.?Belongie.?Robust?Object?Tracking?with?Online?Multiple?Instance?Learning.?PAMI,?33(7):1619–1632,?2011.?

然而，当跟踪器丢失目标时，输出的位置可能是随机的，平均误差可能不能正确的度量跟踪性能。

Recently?the?precision?plot?[6,?27]?has?been?adopted?to?measure?the?overall?tracking?performance.?

References——精度图

[6]?B.?Babenko,?M.-H.?Yang,?and?S.?Belongie.?Robust?Object?Tracking?with?Online?Multiple?Instance?Learning.?PAMI,?33(7):1619–1632,?2011.?

[27]?F.?Henriques,?R.?Caseiro,?P.?Martins,?and?J.?Batista.?Exploiting?the?Circulant?Structure?of?Tracking-by-Detection?with?Kernels.?In?ECCV,?2012.?

最近，精度图已经被用来度量整体的跟踪性能。

It?shows?the?percentage?of?frames?whose?estimated?location?is?within?the?given?threshold?distance?of?the?ground?truth.?

它显示了估计位置在实际举例的阈值内的帧的百分比。

As?the?representative?precision?score?for?each?tracker?we?use?the?score?for?the?threshold?=?20?pixels?[6].

我们用20个像素作为每个跟踪器的表示准确分数的阈值。

段落

Success?plot.?Another?evaluation?metric?is?the?bounding?box?overlap.?Given?the?tracked?bounding?box?rt?and?the?ground?truth?bounding?box?ra,?the?overlap?score?is?defined??，where??and??represent?the?intersection?and?union?of?two?regions,?respectively,?and??denotes?the?number?of?pixels?in?the?region.?

成功图。另一个评估的度量标准是边界框的重叠率。给定跟踪边界框rt和实际标注的边界框ra，重叠分数被定义为，和分别表示两个区域的交和并，表示这个区域的像素数。

To?measure?the?performance?on?a?sequence?of?frames,?we?count?the?number?of?successful?frames?whose?overlap?S?is?larger?than?the?given?threshold?to.?

为了度量整体图像序列的性能，我们统计成功帧的数目，成功帧是重叠率S比给定阈值大。

The?success?plot?shows?the?ratios?of?successful?frames?at?the?thresholds?varied?from?0?to?1.?

成功图显示了成功帧的比例，阈值为0-1。

Using?one?success?rate?value?at?a?specific?threshold?(e.g.?to=0.5)?for?tracker?evaluation?may?not?be?fair?or?representative.?

用一个在特定阈值的成功率(例如to=0.5)来评估跟踪器可能是不公平的或不具有代表性的。

Instead?we?use?the?area?under?curve?(AUC)?of?each?success?plot?to?rank?the?tracking?algorithms.

作为代替，我们用每个成功图的AUC来对跟踪算法进行排序。

段落

Robustness?Evaluation.?The?conventional?way?to?evaluate?trackers?is?to?run?them?throughout?a?test?sequence?with?initialization?from?the?ground?truth?position?in?the?first?frame?and?report?the?average?precision?or?success?rate.?

鲁棒性评估。传统的评估跟踪器的方法是用第一帧的实际标准位置作为初始化，然后在整个测试序列上运行它们，得到一个平均精度或成功率。

We?refer?this?as?one-pass?evaluation?(OPE).?However?a?tracker?may?be?sensitive?to?the?initialization,?and?its?performance?with?different?initialization?at?a?different?start?frame?may?become?much?worse?or?better.?

我们把这个看作是一个评估(OPE)。然而一个跟踪器可能对初始化是敏感的，在不同的帧的不同的初始化可能会更糟或更好。

Therefore,?we?propose?two?ways?to?analyze?a?tracker’s?robustness?to?initialization,?by?perturbing?the?initialization?temporally?(i.e.,?start?at?different?frames)?and?spatially?(i.e.,?start?by?different?bounding?boxes).?

因此，我们提出了两种初始化方式来分析一个跟踪器的鲁棒性，通过从时间上?(不同的初始帧)和空间上(不同的边框)扰乱初始化。

These?tests?are?referred?as?temporal?robustness?evaluation?(TRE)?and?spatial?robustness?evaluation?(SRE)?respectively.

这些测试分别被看作是时间鲁棒性估计(TRE)和空间鲁棒性估计(SRE)。

段落

The?proposed?test?scenarios?happen?a?lot?in?the?real-world?applications?as?a?tracker?is?often?initialized?by?an?object?detector,?which?is?likely?to?introduce?initialization?errors?in?terms?of?position?and?scale.?

当跟踪器经常被一个目标检测器初始化时，提出的测试场景在现实世界应用是经常发生的，考虑到位置和尺度这可能引入初始化误差。

In?addition,?an?object?detector?may?be?used?to?re-initialize?a?tracker?at?different?time?instances.?

此外，一个目标检测器可能在不同的时间实例中用来重新初始化一个跟踪器。

By?investigating?a?tracker’s?characteristic?in?the?robustness?evaluation,?more?thorough?understanding?and?analysis?of?the?tracking?algorithm?can?be?carried?out.

通过在鲁棒性估计中研究一个跟踪器的特性，能更全面的了解和分析要运行的跟踪算法。

Temporal?Robustness?Evaluation.?Given?one?initial?frame?together?with?the?ground-truth?bounding?box?of?target,?one?tracker?is?initialized?and?runs?to?the?end?of?the?sequence,?i.e.,?one?segment?of?the?entire?sequence.?

时间鲁棒性估计。给定一个有实际目标框的初始帧，一个跟踪器被初始化并运行到序列结束，例如，整个序列的一部分。

The?tracker?is?evaluated?on?each?segment,?and?the?overall?statistics?are?tallied.?

跟踪器在每部分上进行评估，进行全面的统计。

Spatial?Robustness?Evaluation.?We?sample?the?initial?bounding?box?in?the?first?frame?by?shifting?or?scaling?the?ground?truth.

空间鲁棒性估计。我们在第一帧采样初始化边框通过漂移或尺度化实际边框。

Here,?we?use?8?spatial?shifts?including?4?center?shifts?and?4?corner?shifts,?and?4?scale?variations?(supplement).?

我们用4个中心漂移和4个边角漂移和4个尺度变化。

The?amount?for?shift?is?10%?of?target?size,?and?the?scale?ratio?varys?among?0.8,?0.9,?1.1?and?1.2?to?the?ground?truth.?

漂移总数是目标尺寸的10%，尺度比变化为实际值的0.8，0.9，1.1，1.2。

Thus,?we?evaluate?each?tracker?12?times?for?SRE.

因此，我们进行空间鲁棒性估计对每个跟踪器测试12次。

5、Evaluation?Results

段落

For?each?tracker,?the?default?parameters?with?the?source?code?are?used?in?all?evaluations.?

对每个跟踪器，源码的默认参数用在所有评估中。

Table?1?lists?the?average?FPS?of?each?tracker?in?OPE?running?on?a?PC?with?Intel?i7?3770?CPU?(3.4GHz).?

表一列出了在PC上每个跟踪器在一次估计中的平均帧率。

More?detailed?speed?statistics,?such?as?minimum?and?maximum,?are?available?in?the?supplement.

更多速度统计细节，例如最小值和最大值，可在补充材料中得到。

段落

For?OPE,?each?tracker?is?tested?on?more?than?29,000?frames.

对于一次测试，每个跟踪器测试了超过29000帧。

For?SRE,?each?tracker?is?evaluated?12?times?on?each?sequence,?where?more?than?350,000?bounding?box?results?are?generated.?

对于空间鲁棒性估计，每个跟踪器在每个序列上评估了12次，产生了超过350000个边界框。

For?TRE,?each?sequence?is?partitioned?into?20?segments?and?thus?each?tracker?is?performed?on?around?310,000?frames.?

对于时间鲁棒性估计，每个序列被分割成20部分，因此每个跟踪器运行了大约310000帧。

To?the?best?of?our?knowledge,?this?is?the?largest?scale?performance?evaluation?of?visual?tracking.?

据我们所知，这是视觉跟踪最大的性能估计。

We?report?the?most?important?findings?in?this?manuscript?and?more?details?and?figures?can?be?found?in?the?supplement.

我们在手稿中记录了最重要的发现，更多的细节和数字可在补充材料中找到。

5.1.?Overall?Performance

The?overall?performance?for?all?the?trackers?is?summarized?by?the?success?and?precision?plots?as?shown?in?Fig?ure?3?where?only?the?top?10?algorithms?are?presented?for?clarity?and?the?complete?plots?are?displayed?in?the?supplementary?material.?

图3中的成功图和精度图总结了所有跟踪器的整体性能，仅清晰的列出了前10个算法，整体图在补充材料中显示。

For?success?plots,?we?use?AUC?scores?to?summarize?and?rank?the?trackers,?while?for?precision?plots?we?use?the?results?at?error?threshold?of?20?for?ranking.

对于成功图，我们用AUC分数来概括和排序跟踪器，而对于精度图我们用误差阈值来排序。

?In?the?precision?plots,?the?rankings?of?some?trackers?are?slightly?different?from?the?rankings?in?the?success?plots?in?that?they?are?based?on?different?metrics?which?measure?different?characteristics?of?trackers.?

在精度图，一些跟踪器的排序与成功图中的排序有一点不同，他们是基于不同的度量标准的，不同的度量标准用来衡量跟踪器的不同特性。

Because?the?AUC?score?of?success?plot?measures?the?overall?performance?which?is?more?accurate?than?the?score?at?one?threshold?of?the?plot,?in?the?following?we?mainly?analyze?the?rankings?based?on?success?plots?but?use?the?precision?plots?as?auxiliary.

因为成功图的AUC分数度量了整体性能，整体性能是比一个阈值更准确的，接下来我们主要分析基于成功图的排序但用精度图作为辅助。

段落

The?average?TRE?performance?is?higher?than?that?of?OPE?in?that?the?number?of?frames?decreases?from?the?first?to?last?segment?of?TRE.?

平均时间鲁棒性性能比一次的时间鲁棒性性能更高，从TRE的第一部分到最后一部分帧的数目是下降的。

As?the?trackers?tend?to?perform?well?in?shorter?sequences,?the?average?of?all?the?results?in?TRE?tend?to?be?higher.?

由于跟踪器趋向于在更多的序列上性能更好，TRE所有结果的平均值趋向于更高。

On?the?other?hand,?the?average?performance?of?SRE?is?lower?than?that?of?OPE.?

另一方面，SRE的平均性能比OPE的平均性能更低。

The?initialization?errors?tend?to?cause?trackers?to?update?with?imprecise?appearance?information,?thereby?causing?gradual?drifts.

初始化误差趋向于引起跟踪器用不准确的外观信息更新，因此会引起逐渐的漂移。

段落

In?the?success?plots,?the?top?ranked?tracker?SCM?in?OPE?outperforms?Struck?by?2.6%?but?is?1.9%?below?Struck?in?SRE.?

在成功图中，排序考前的跟踪器SCM在在OPE比Struck好2.6%但在SRE中比Struck低1.9%。

The?results?also?show?that?OPE?is?not?the?best?performance?indicator?as?the?OPE?is?one?trial?of?SRE?or?TRE.?

这些结果也显示了OPE不是最好的性能指示器，因为OPE是SRE或TRE的一次尝试。

The?ranking?of?TLD?in?TRE?is?lower?than?OPE?and?SRE.?

TLD在TRE中的排名比在OPE和SRE中更低。

This?is?because?TLD?performs?well?in?long?sequences?with?a?redetection?module?while?there?are?numerous?short?segments?in?TRE.?

这是因为TLD有一个再检测模块，在长的序列中性能更好，而在TRE中有许多短序列。

The?success?plots?of?Struck?in?TRE?and?SRE?show?that?the?success?rate?of?Struck?is?higher?than?SCM?and?ALSA?when?the?overlap?threshold?is?small,?but?less?than?SCM?and?ALSA?when?the?overlap?threshold?is?large.?

当重叠阈值较小时Struck的成功图在TRE和SRE中表明Struck的成功率比SCM和ALSA更高，但重叠阈值较大时，Struck的成功率更低。

This?is?because?Struck?only?estimates?the?location?of?target?and?does?

not?handle?scale?variation.

这是因为Struck仅估计目标的位置不处理尺度变化。

段落

Sparse?representations?are?used?in?SCM,?ASLA,?LSK,?MTT?and?L1APG.?

稀疏表示在SCM，ASLA，LSK，MTT和L1APG中使用了。

These?trackers?perform?well?in?SRE?and?TRE,?which?suggests?sparse?representations?are?effective?models?to?account?for?appearance?change?(e.g.,?occlusion).?

这些跟踪器在SRE和TRE中运行很好，这表明稀疏表示是解释外观变化(例如遮挡)的有效模型。

We?note?that?SCM,?ASLA?and?LSK?outperform?MTT?and?L1APG.

我们注意到SCM，ASLA，LSK超过了MTT和L1APG。

?The?results?suggest?that?local?sparse?representations?are?more?effective?than?the?ones?with?holistic?sparse?templates.?

这些结果表明局部稀疏表示比整体稀疏模版更有效。

The?AUC?score?of?ASLA?deceases?less?than?the?other?top?5?trackers?from?OPE?to?SRE?and?the?ranking?of?ASLA?also?increases.?

从OPE到SRE时，ASLA的AUC分数下降的比其它的前5个跟踪器要少，ASLA的排名也升高了。

It?indicates?the?alignment-pooling?technique?adopted?by?ASLA?is?more?robust?to?misalignments?and?background?clutters.

它表明ASLA的alignment-pooling技术比不校准和杂乱背景更鲁棒。

段落

Among?the?top?10?trackers,?CSK?has?the?highest?speed?where?the?proposed?circulant?structure?plays?a?key?role.?

在前10个跟踪器中，CSK的速度最快，其中的循环结构是非常重要的。

The?VTD?and?VTS?methods?adopt?mixture?models?to?improve?the?tracking?performance.?

VTD和VTS方法采用了混合模型来改善跟踪性能。

Compared?with?other?higher?ranked?trackers,?the?performance?bottleneck?of?them?can?be?attributed?to?their?adopted?representation?based?on?sparse?principal?component?analysis,?where?the?holistic?templates?are?used.?

与其它的排名更高的跟踪器相比，他们的性能瓶颈是因为他们采用的稀疏PCA和使用的整体模版。

Due?to?the?space?limitation,?the?plots?of?SRE?are?presented?for?analysis?in?the?following?sections,?and?more?results?are?included?in?the?supplement.

由于空间限制，SRE图的分析在接下来的部分，更多的结果在补充材料中。

5.2.?Attributebased?Performance?Analysis

By?annotating?the?attributes?of?each?sequence,?we?construct?subsets?with?different?dominant?attributes?which?facilitates?analyzing?the?performance?of?trackers?for?each?challenging?factor.?

通过标注每个序列的属性，我们构建了有不同主导属性的子集，这有利于分析跟踪器在每个挑战性因素上的性能。

Due?to?space?limitations,?we?only?illustrate?and?analyze?the?success?plots?and?precision?plots?of?SRE?for?attributes?OCC,?SV,?and?FM?as?shown?in?Figure?4,?and?more?results?are?presented?in?the?supplementary?material.

由于空间限制，我们仅说明和分析了属性OCC，SV和FM在SRE上的成功图，如图4所示，更多的结果在补充材料中。

When?an?object?moves?fast,?dense?sampling?based?trackers?(e.g.,?Struck,?TLD?and?CXT)?perform?much?better?than?others.?

当目标运动很快时，基于密集采样的跟踪器(例如Struck，TLD和CXT)性能比其它的更好。

One?reason?is?that?the?search?ranges?are?large?and?the?discriminative?models?are?able?to?discriminate?the?targets?from?the?background?clutters.?

一个原因是搜索机制更大并且判别式模型能从杂乱背景中区分出目标。

However,?the?stochastic?search?based?trackers?with?high?overall?performance?(e.g.,?SCM?and?ASLA)?do?not?perform?well?in?this?subset?due?to?the?poor?dynamic?models.?

然而，由于差的动态模型，更高性能的基于统计搜索的跟踪器(例如SCM和ASLA)在子集中不能运行的很好。

If?these?parameters?are?set?to?large?values,?more?particles?are?required?to?make?the?tracker?stable.

如果这些参数被设置为大的值，更多的粒子要求跟踪器更稳定。

?These?trackers?can?be?further?improved?with?dynamic?models?with?more?effective?particle?filters.

这些跟踪器能用更有效的粒子滤波的动态模型进行进一步改进。

段落

On?the?OCC?subset,?the?Struck,?SCM,?TLD,?LSK?and?ASLA?methods?outperform?others.?

在OCC子集中，Struck,?SCM,?TLD,?LSK?和ASLA方法比其它的表现更好。

The?results?suggest?that?structured?learning?and?local?sparse?representations?are?effective?in?dealing?with?occlusions.

这些结果表明结构化学习和局部稀疏表示能有效的处理遮挡。

?On?the?SV?subset,?ASLA,?SCM?and?Struck?perform?best.?

在SV子集中，ASLA,?SCM和?Struck运行最好。

The?results?show?that?trackers?with?affine?motion?models?(e.g.,?ASLA?and?SCM)?often?handle?scale?variation?better?than?others?that?are?designed?to?account?for?only?translational?motion?with?a?few?exceptions?such?as?Struck.

结果表明，有仿射运动模型的跟踪器(例如ASLA和SCM)经常比其它那些仅考虑平移运动变换的方法(例如Struck)处理尺度变化更好。

5.3.?Initialization?with?Different?Scale

It?has?been?known?that?trackers?are?often?sensitive?to?initialization?variations.

正如我们知道的那样，跟踪器对初始化的变化经常是很敏感的。

?Figure?5?and?Figure?6?show?the?summarized?tracking?performance?with?initialization?at?different?scales.?

图5和图6显示了归纳的用不同尺度初始化时的跟踪性能。

When?computing?the?overlap?score,?we?rescale?the?tracking?results?so?that?the?performance?summary?could?be?comparable?with?the?original?scale,?i.e.,?the?plots?of?OPE?in?Figure?3.?

当计算重叠分数时，我们再缩放了跟踪结果以便性能概括能与原始尺度比较，例如，图3中的?OPE图。

Figure?6?illustrates?the?average?performance?of?all?trackers?for?each?scale?which?shows?the?performance?often?decreases?significantly?when?the?scale?factor?is?large?(e.g.,?*1.2)?as?many?background?pixels?are?inevitably?included?in?the?initial?representations.?

图6说明了所有跟踪器对每个尺度的平均性能，这表明当尺度因子较大时，性能经常会明显下降，因为许多背景像素是不可避免的包含在初始化表示中。

The?performance?of?TLD,?CXT,?DFT?and?LOT?decreases?with?the?increase?of?initialization?scale.?

初始化尺度增大时，TLD,?CXT,?DFT?和LOT的性能下降了。

This?indicates?these?trackers?are?more?sensitive?to?background?clutters.?

这表明这些跟踪器对于杂乱背景是更敏感的。

Some?trackers?perform?better?when?the?scale?factor?is?smaller,?such?as?L1APG,?MTT,?LOT?and?CPF.?

一些跟踪器性能更好当尺度因子很小的时候，例如L1APG,?MTT,?LOT?和CPF。

One?reason?for?this?in?the?case?of?L1APG?and?MTT?is?that?the?templates?have?to?be?warped?to?fit?the?size?of?the?usually?smaller?canonical?template?so?that?if?the?initial?template?is?small,?more?appearance?details?will?be?kept?in?the?model.?

L1APG?和MTT当尺度因子很小时运行更好的一个原因是模版已经扭曲到适应通常小的标准模版的尺寸，以便当初始化模版是小的时将更多的外观细节保留在模型中。

On?the?other?hand,?some?trackers?perform?well?or?even?better?when?the?initial?bounding?box?is?enlarged,?such?as?Struck,?OAB,?SemiT,?and?BSBT.?

另一方面，当初始化边框增大时，一些跟踪器运行很好或者甚至更好，例如Struck,?OAB,?SemiT和BSBT。

This?indicates?that?the?Haar-like?features?are?somewhat?robust?to?background?clutters?due?to?the?summation?operations?when?computing?features.?

这表明，由于计算特征时的求和操作，Haar-like特征对杂乱背景更鲁棒。

Overall,?Struck?is?less?sensitive?to?scale?variation?than?other?well-performing?methods.

总而言之，与其它的性能较好的方法相比，Struck对尺度变化是更不敏感的。

6.?Concluding?Remarks

?In?this?paper,?we?carry?out?large?scale?experiments?to?evaluate?the?performance?of?recent?online?tracking?algorithms.?

在这篇论文中，我们完成了大量的实验来评估最新的在线跟踪算法的性能。

Based?on?our?evaluation?results?and?observations,?we?highlight?some?tracking?components?which?are?essential?for?improving?tracking?performance.?

基于我们的评估结果和观察，我们强调了一些改善跟踪性能所必须的部分。

First,?background?information?is?critical?for?effective?tracking.

首先，背景信息对于有效跟踪是非常关键的。

?It?can?be?exploited?by?using?advanced?learning?techniques?to?encode?the?background?information?in?the?discriminative?model?implicitly?(e.g.,?Struck),?or?serving?as?the?tracking?context?explicitly?(e.g.,?CXT).

在判别式模型中它可以用先进的学习方法来隐式的编码背景信息(例如Struck)，或显示的使用跟踪上下文(例如CXT)。

?Second,?local?models?are?important?for?tracking?as?shown?in?the?performance?improvement?of?local?sparse?representation?(e.g.,?ASLA?and?SCM)?compared?with?the?holistic?sparse?representation?(e.g.,?MTT?and?

L1APG).?

第二，局部模型对于跟踪是很重要的，因为局部稀疏表示(例如ASLA和SCM)相比于整体稀疏表示性能(例如MTT和L1APG)得到了提高。

They?are?particularly?useful?when?the?appearance?of?target?is?partially?changed,?such?as?partial?occlusion?or?deformation.?

当目标的外观是部分变化的时候他们是特别有用的，例如部分遮挡或形变。

Third,?motion?model?or?dynamic?model?is?crucial?for?object?tracking,?especially?when?the?motion?of?target?is?large?or?abrupt.?

第三，运动模型或动态模型对于目标跟踪是关键的，尤其是目标运动大或突然的时候。

However,?most?of?our?evaluated?trackers?do?not?focus?on?this?component.?

然而，我们评估的跟踪器的大多数不能集中在这部分上。

Good?location?prediction?based?on?the?dynamic?model?could?reduce?the?search?range?and?thus?improve?the?tracking?efficiency?and?robustness.?

基于动态模型的好的位置预测能降低搜索变化，因此提高了跟踪性能和鲁棒性。

Improving?these?components?will?further?advance?the?state?of?the?art?of?online?object?tracking.

改进这些组成部分能进一步发展目前的在线目标跟踪。

段落

The?evaluation?results?show?that?significant?progress?in?the?field?of?object?tracking?has?been?made?in?the?last?decade.?

这些评估结果表明在过去十年目标跟踪领域已经取得了明显的进步。

We?propose?and?demonstrate?evaluation?metrics?for?in-depth?analysis?of?tracking?algorithms?from?several?perspectives.?

我们提出和证明了深度分析跟踪算法的评估指标。

This?large?scale?performance?evaluation?facilitates?better?understanding?of?the?state-of-the-art?online?object?tracking?approaches,?and?provides?a?platform?for?gauging?new?algorithms.?

大量的性能评估有利于更好的理解目前的在线目标跟踪算法，并提供了一个估计新方法的平台。

Our?ongoing?work?focuses?on?extending?the?dataset?and?code?library?to?include?more?fully?annotated?sequences?and?trackers.

为了更全面的标注数据集和跟踪器，我们不间断的工作主要集中在扩大数据集和编码库上。

Acknowledgment.

We?thank?the?reviewers?for?valuable?comments?and?suggestions.?

我们感谢审稿人的有价值的评论和建议。

The?work?is?supported?partly?by?NSF?CAREER?Grant?#1149783?and?NSF?IIS?Grant?#1152576.?

这个工作部分由NSF?CAREER?Grant?#1149783和NSF?IIS?Grant?#1152576支持。

Wu?is?also?with?Nanjing?University?of?Information?Science?and?Technology,?China?and?supported?partly?by?NSFC?Grant?#61005027.

Wu由中国南京信息工程大学NSFC?Grant?#61005027支持。

References?