MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
| Autor: | Aljosa Osep, Ian Reid, Daniel Cremers, Konrad Schindler, Stefan Roth, Patrick Dendorfer, Laura Leal-Taixé, Anton Milan |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
BitTorrent tracker Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Multi-object-tracking Evaluation MOTChallenge Computer vision MOTA Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Visibility business.industry Deep learning 05 social sciences 050301 education Object (computer science) ddc Video tracking Pattern recognition (psychology) Benchmark (computing) Robot 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business 0503 education Software |
| Zdroj: | International Journal of Computer Vision, 129 (4) |
| ISSN: | 0920-5691 1573-1405 |
| Popis: | 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. International Journal of Computer Vision, 129 (4) ISSN:0920-5691 ISSN:1573-1405 |
| Databáze: | OpenAIRE |
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