MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking

Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for rese...

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Veröffentlicht in:	International journal of computer vision Jg. 129; H. 4; S. 845 - 881
Hauptverfasser:	Dendorfer, Patrick, Os̆ep, Aljos̆a, Milan, Anton, Schindler, Konrad, Cremers, Daniel, Reid, Ian, Roth, Stefan, Leal-Taixé, Laura
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.04.2021 Springer Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Autonomous cars Benchmarks Cameras Computer Imaging Computer Science Computer vision Error analysis Image Processing and Computer Vision Labels Machine learning Machine vision Multiple target tracking Pattern Recognition Pattern Recognition and Graphics Pedestrians Performance evaluation Special Issue on Performance Evaluation in Computer Vision Visibility Vision Evaluation Computer vision MOTA MOTChallenge Multi-object-tracking
ISSN:	0920-5691, 1573-1405
Online-Zugang:	Volltext
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Abstract	Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present MOTChallenge , a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data and create a framework for the standardized evaluation of multiple object tracking methods. The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community, with applications ranging from robot navigation to self-driving cars. This paper collects the first three releases of the benchmark: (i) MOT15 , along with numerous state-of-the-art results that were submitted in the last years, (ii) MOT16 , which contains new challenging videos, and (iii) MOT17 , that extends MOT16 sequences with more precise labels and evaluates tracking performance on three different object detectors. The second and third release not only offers a significant increase in the number of labeled boxes, but also provide labels for multiple object classes beside pedestrians, as well as the level of visibility for every single object of interest. We finally provide a categorization of state-of-the-art trackers and a broad error analysis. This will help newcomers understand the related work and research trends in the MOT community, and hopefully shed some light into potential future research directions.
AbstractList	Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present MOTChallenge , a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data and create a framework for the standardized evaluation of multiple object tracking methods. The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community, with applications ranging from robot navigation to self-driving cars. This paper collects the first three releases of the benchmark: (i) MOT15 , along with numerous state-of-the-art results that were submitted in the last years, (ii) MOT16 , which contains new challenging videos, and (iii) MOT17 , that extends MOT16 sequences with more precise labels and evaluates tracking performance on three different object detectors. The second and third release not only offers a significant increase in the number of labeled boxes, but also provide labels for multiple object classes beside pedestrians, as well as the level of visibility for every single object of interest. We finally provide a categorization of state-of-the-art trackers and a broad error analysis. This will help newcomers understand the related work and research trends in the MOT community, and hopefully shed some light into potential future research directions. Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data and create a framework for the standardized evaluation of multiple object tracking methods. The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community, with applications ranging from robot navigation to self-driving cars. This paper collects the first three releases of the benchmark: (i) MOT15, along with numerous state-of-the-art results that were submitted in the last years, (ii) MOT16, which contains new challenging videos, and (iii) MOT17, that extends MOT16 sequences with more precise labels and evaluates tracking performance on three different object detectors. The second and third release not only offers a significant increase in the number of labeled boxes, but also provide labels for multiple object classes beside pedestrians, as well as the level of visibility for every single object of interest. We finally provide a categorization of state-of-the-art trackers and a broad error analysis. This will help newcomers understand the related work and research trends in the MOT community, and hopefully shed some light into potential future research directions.
Audience	Academic
Author	Roth, Stefan Cremers, Daniel Reid, Ian Milan, Anton Os̆ep, Aljos̆a Schindler, Konrad Dendorfer, Patrick Leal-Taixé, Laura
Author_xml	– sequence: 1 givenname: Patrick orcidid: 0000-0002-4623-8749 surname: Dendorfer fullname: Dendorfer, Patrick email: patrick.dendorfer@tum.de organization: Technical University Munich – sequence: 2 givenname: Aljos̆a surname: Os̆ep fullname: Os̆ep, Aljos̆a organization: Technical University Munich – sequence: 3 givenname: Anton surname: Milan fullname: Milan, Anton organization: Amazon Research – sequence: 4 givenname: Konrad surname: Schindler fullname: Schindler, Konrad organization: ETH Zürich – sequence: 5 givenname: Daniel surname: Cremers fullname: Cremers, Daniel organization: Technical University Munich – sequence: 6 givenname: Ian surname: Reid fullname: Reid, Ian organization: The University of Adelaide – sequence: 7 givenname: Stefan surname: Roth fullname: Roth, Stefan organization: Technical University of Darmstadt – sequence: 8 givenname: Laura surname: Leal-Taixé fullname: Leal-Taixé, Laura organization: Technical University Munich
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SubjectTerms	Algorithms Artificial Intelligence Autonomous cars Benchmarks Cameras Computer Imaging Computer Science Computer vision Error analysis Image Processing and Computer Vision Labels Machine learning Machine vision Multiple target tracking Pattern Recognition Pattern Recognition and Graphics Pedestrians Performance evaluation Special Issue on Performance Evaluation in Computer Vision Visibility Vision
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Title	MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
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