TU Darmstadt / ULB / TUbiblio

MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking

Dendorfer, Patrick ; Os̆ep, Aljos̆a ; Milan, Anton ; Schindler, Konrad ; Cremers, Daniel ; Reid, Ian ; Roth, Stefan ; Leal-Taixé, Laura (2020):
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking.
In: International Journal of Computer Vision, Springer Nature, ISSN 0920-5691,
DOI: 10.1007/s11263-020-01393-0,
[Article]

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.

Item Type: Article
Erschienen: 2020
Creators: Dendorfer, Patrick ; Os̆ep, Aljos̆a ; Milan, Anton ; Schindler, Konrad ; Cremers, Daniel ; Reid, Ian ; Roth, Stefan ; Leal-Taixé, Laura
Title: MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
Language: English
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.

Journal or Publication Title: International Journal of Computer Vision
Publisher: Springer Nature
Uncontrolled Keywords: Evaluation, Computer vision
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Visual Inference
Date Deposited: 16 Mar 2021 08:52
DOI: 10.1007/s11263-020-01393-0
Additional Information:

Special Issue on Performance Evaluation in Computer Vision

Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details