FastTrackTr:Towards Fast Multi-Object Tracking with Transformers
This work addresses the speed bottleneck in multi-object tracking for applications like video surveillance, though it is incremental as it builds on existing joint detection and tracking approaches.
The paper tackles the slow inference speed of transformer-based multi-object tracking methods by proposing FastTrackTr, a framework that integrates joint detection and tracking with efficient information transfer, achieving competitive accuracy and potential real-time performance on multiple datasets.
Transformer-based multi-object tracking (MOT) methods have captured the attention of many researchers in recent years. However, these models often suffer from slow inference speeds due to their structure or other issues. To address this problem, we revisited the Joint Detection and Tracking (JDT) method by looking back at past approaches. By integrating the original JDT approach with some advanced theories, this paper employs an efficient method of information transfer between frames on the DETR, constructing a fast and novel JDT-type MOT framework: FastTrackTr. Thanks to the superiority of this information transfer method, our approach not only reduces the number of queries required during tracking but also avoids the excessive introduction of network structures, ensuring model simplicity. Experimental results indicate that our method has the potential to achieve real-time tracking and exhibits competitive tracking accuracy across multiple datasets.