3 research outputs found
Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads
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.Comment: Accepted at AJCAI 202