Real-time Multi-Object Tracking Based on Bi-directional Matching
This work addresses occlusion handling for real-time multi-object tracking in computer vision, offering an incremental improvement over existing methods.
The paper tackles occlusion challenges in multi-object tracking by introducing a bi-directional matching algorithm with a stranded area to store temporarily lost objects, improving trajectory continuity; it achieves 63.4% MOTA, 55.3% IDF1, and 20.1 FPS on MOT17.
In recent years, anchor-free object detection models combined with matching algorithms are used to achieve real-time muti-object tracking and also ensure high tracking accuracy. However, there are still great challenges in multi-object tracking. For example, when most part of a target is occluded or the target just disappears from images temporarily, it often leads to tracking interruptions for most of the existing tracking algorithms. Therefore, this study offers a bi-directional matching algorithm for multi-object tracking that makes advantage of bi-directional motion prediction information to improve occlusion handling. A stranded area is used in the matching algorithm to temporarily store the objects that fail to be tracked. When objects recover from occlusions, our method will first try to match them with objects in the stranded area to avoid erroneously generating new identities, thus forming a more continuous trajectory. Experiments show that our approach can improve the multi-object tracking performance in the presence of occlusions. In addition, this study provides an attentional up-sampling module that not only assures tracking accuracy but also accelerates training speed. In the MOT17 challenge, the proposed algorithm achieves 63.4% MOTA, 55.3% IDF1, and 20.1 FPS tracking speed.