Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads
This work addresses the challenge of multiple object tracking in extremely crowded scenes, which is critical for applications like surveillance and crowd management, but it is incremental as it builds on existing detection and tracking methods.
The paper tackles the problem of tracking pedestrians in dense crowds with heavy occlusion by focusing on heads, using a joint head and body detector that learns head-body ratios dynamically, resulting in significant improvements in recall and precision for small and medium-sized pedestrians and achieving state-of-the-art results on datasets like MOT20.
With the rapid development of deep learning, object detection and tracking play a vital role in today's society. Being able to identify and track all the pedestrians in the dense crowd scene with computer vision approaches is a typical challenge in this field, also known as the Multiple Object Tracking (MOT) challenge. Modern trackers are required to operate on more and more complicated scenes. According to the MOT20 challenge result, the pedestrian is 4 times denser than the MOT17 challenge. Hence, improving the ability to detect and track in extremely crowded scenes is the aim of this work. In light of the occlusion issue with the human body, the heads are usually easier to identify. In this work, we have designed a joint head and body detector in an anchor-free style to boost the detection recall and precision performance of pedestrians in both small and medium sizes. Innovatively, our model does not require information on the statistical head-body ratio for common pedestrians detection for training. Instead, the proposed model learns the ratio dynamically. To verify the effectiveness of the proposed model, we evaluate the model with extensive experiments on different datasets, including MOT20, Crowdhuman, and HT21 datasets. As a result, our proposed method significantly improves both the recall and precision rate on small & medium sized pedestrians and achieves state-of-the-art results in these challenging datasets.