CVPR19 Tracking and Detection Challenge: How crowded can it get?
arXiv:1906.04567
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.
arXiv admin note: substantial text overlap with arXiv:1603.00831, arXiv:1504.01942
References in corpus (2)
Cited by in corpus (15)
- Deep Learning in Video Multi-Object Tracking: A Survey
- JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments
- Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy
- Rank-based verification for long-term face tracking in crowded scenes
- Online Multiple Pedestrians Tracking using Deep Temporal Appearance Matching Association
- Towards pedestrian head tracking: A benchmark dataset and a multi-source data fusion network
- Graph-Based Multi-Camera Soccer Player Tracker
- Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking
- Towards Streaming Perception
- Towards in-store multi-person tracking using head detection and track heatmaps
- PANDA: A Gigapixel-level Human-centric Video Dataset
- RoadText-1K: Text Detection & Recognition Dataset for Driving Videos
- 3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset
- A Multilevel Strategy to Improve People Tracking in a Real-World Scenario
- Localization-Based Tracking