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CVPR19 Tracking and Detection Challenge: How crowded can it get? | | Patrick Dendorfer
; Hamid Rezatofighi
; Anton Milan
; Javen Shi
; Daniel Cremers
; Ian Reid
; Stefan Roth
; Konrad Schindler
; Laura Leal-Taixe
; | | Date: |
10 Jun 2019 | | Abstract: | Standardized benchmarks are crucial for the majority of computer vision
applications. Although leaderboards and ranking tables should not be
over-claimed, benchmarks often provide the most objective measure of
performance and are therefore important guides for research.
The benchmark for Multiple Object Tracking, MOTChallenge, was launched with
the goal to establish a standardized evaluation of multiple object tracking
methods. The challenge focuses on multiple people tracking, since pedestrians
are well studied in the tracking community, and precise tracking and detection
has high practical relevance. Since the first release, MOT15, MOT16 and MOT17
have tremendously contributed to the community by introducing a clean dataset
and precise framework to benchmark multi-object trackers. In this paper, we
present our CVPR19 benchmark, consisting of 8 new sequences depicting very
crowded challenging scenes. The benchmark will be presented at the 4th BMTT MOT
Challenge Workshop at the Computer Vision and Pattern Recognition Conference
(CVPR) 2019, and will evaluate the state-of-the-art in multiple object tracking
whend handling extremely crowded scenarios. | | Source: | arXiv, 1906.4567 | | Services: | Forum | Review | PDF | Favorites | |
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