CrowdTrack: A Benchmark for Difficult Multiple Pedestrian Tracking in Real Scenarios
Teng Fu, Yuwen Chen, Zhuofan Chen, Mengyang Zhao, Bin Li, Xiangyang Xue

TL;DR
CrowdTrack is a challenging large-scale dataset of real-world multi-pedestrian tracking scenarios designed to improve algorithm robustness in complex, occluded, and diverse environments, addressing limitations of existing datasets.
Contribution
The paper introduces CrowdTrack, a new large-scale, realistic dataset for multi-pedestrian tracking in complex scenarios, and provides comprehensive analysis and benchmarking of state-of-the-art models.
Findings
Existing models struggle with complex scenarios in CrowdTrack.
Foundation models show varied performance on the dataset.
CrowdTrack enables development of more robust tracking algorithms.
Abstract
Multi-object tracking is a classic field in computer vision. Among them, pedestrian tracking has extremely high application value and has become the most popular research category. Existing methods mainly use motion or appearance information for tracking, which is often difficult in complex scenarios. For the motion information, mutual occlusions between objects often prevent updating of the motion state; for the appearance information, non-robust results are often obtained due to reasons such as only partial visibility of the object or blurred images. Although learning how to perform tracking in these situations from the annotated data is the simplest solution, the existing MOT dataset fails to satisfy this solution. Existing methods mainly have two drawbacks: relatively simple scene composition and non-realistic scenarios. Although some of the video sequences in existing dataset do…
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