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

arXiv: https://arxiv.org/abs/1906.04567 · HTML: https://mlsanity.com/paper/1906.04567/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 22 of 22)

- [Tracking Pedestrian Heads in Dense Crowd](https://mlsanity.com/paper/2103.13516/) (2021)
- [A Bilingual, OpenWorld Video Text Dataset and End-to-end Video Text Spotter with Transformer](https://mlsanity.com/paper/2112.04888/) (2021)
- [Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking](https://mlsanity.com/paper/2101.12159/) (2021)
- [Minkowski Tracker: A Sparse Spatio-Temporal R-CNN for Joint Object Detection and Tracking](https://mlsanity.com/paper/2208.10056/) (2022)
- [ICDAR 2023 Video Text Reading Competition for Dense and Small Text](https://mlsanity.com/paper/2304.04376/) (2023)
- [Understanding Ethics, Privacy, and Regulations in Smart Video Surveillance for Public Safety](https://mlsanity.com/paper/2212.12936/) (2022)
- [DSText V2: A Comprehensive Video Text Spotting Dataset for Dense and Small Text](https://mlsanity.com/paper/2312.01938/) (2023)
- [Enhancing Thermal MOT: A Novel Box Association Method Leveraging Thermal Identity and Motion Similarity](https://mlsanity.com/paper/2411.12943/) (2024)
- [Rank-based verification for long-term face tracking in crowded scenes](https://mlsanity.com/paper/2107.13273/) (2021)
- [Deep Learning Techniques for Visual Counting](https://mlsanity.com/paper/2206.03033/) (2022)
- [Graph-Based Multi-Camera Soccer Player Tracker](https://mlsanity.com/paper/2211.02125/) (2022)
- [Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking](https://mlsanity.com/paper/2410.02638/) (2024)
- [Learning a Neural Association Network for Self-supervised Multi-Object Tracking](https://mlsanity.com/paper/2411.11514/) (2024)
- [Localization-Based Tracking](https://mlsanity.com/paper/2104.05823/) (2021)
- [Multi-target tracking for video surveillance using deep affinity network: a brief review](https://mlsanity.com/paper/2110.15674/) (2021)
- [STURE: Spatial-Temporal Mutual Representation Learning for Robust Data Association in Online Multi-Object Tracking](https://mlsanity.com/paper/2201.06824/) (2022)
- [People Tracking and Re-Identifying in Distributed Contexts: Extension Study of PoseTReID](https://mlsanity.com/paper/2205.10086/) (2022)
- [mvHOTA: A multi-view higher order tracking accuracy metric to measure spatial and temporal associations in multi-point detection](https://mlsanity.com/paper/2206.09372/) (2022)
- [t-RAIN: Robust generalization under weather-aliasing label shift attacks](https://mlsanity.com/paper/2305.08302/) (2023)
- [A Unified Simulation Framework for Visual and Behavioral Fidelity in Crowd Analysis](https://mlsanity.com/paper/2312.02613/) (2023)
- [A Multilevel Strategy to Improve People Tracking in a Real-World Scenario](https://mlsanity.com/paper/2404.18876/) (2024)
- [Answer Set Programming Energised! End-to-End Neurosymbolic Reasoning and Learning with ASP and Energy Based Models](https://mlsanity.com/paper/2607.08136/) (2026)

## Abstract (arXiv)

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.