Pith. sign in

REVIEW 3 cited by

CVPR19 Tracking and Detection Challenge: How crowded can it get?

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1906.04567 v1 pith:KVWIWJD3 submitted 2019-06-10 cs.CV cs.LG

Patrick Dendorfer , Hamid Rezatofighi , Anton Milan , Javen Shi , Daniel Cremers , Ian Reid , Stefan Roth , Konrad Schindler
show 1 more author
Laura Leal-Taixe
This is my paper · ORCID
classification cs.CVcs.LG
keywords trackingbenchmarkmultiplechallengecrowdedobjectbenchmarkscommunity
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning a Neural Association Network for Self-supervised Multi-Object Tracking

    cs.CV 2024-11 conditional novelty 6.0 of 10

    A self-supervised training method using a neural Kalman filter and Sinkhorn assignment learns to associate detections across frames without identity labels, reaching state-of-the-art self-supervised scores on MOT17 and MOT20.

  2. Answer Set Programming Energised! End-to-End Neurosymbolic Reasoning and Learning with ASP and Energy Based Models

    cs.AI 2026-07 conditional novelty 5.5 of 10

    ASPEn integrates ASP stable-model semantics with energy-based models for joint discrete-continuous optimisation and end-to-end training on visual reasoning and multi-object tracking.

  3. Enhancing Thermal MOT: A Novel Box Association Method Leveraging Thermal Identity and Motion Similarity

    cs.CV 2024-11 conditional novelty 4.0 of 10

    Combining thermal histogram similarity with motion similarity improves ByteTrack and OCSORT MOT scores by about one to two MOTA points on a new RGB-thermal pedestrian dataset.

Pith tools