TransTrack: Multiple-Object Tracking with Transformer
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Multiple-object tracking(MOT) is mostly dominated by complex and multi-step tracking-by-detection algorithm, which performs object detection, feature extraction and temporal association, separately. Query-key mechanism in single-object tracking(SOT), which tracks the object of the current frame by object feature of the previous frame, has great potential to set up a simple joint-detection-and-tracking MOT paradigm. Nonetheless, the query-key method is seldom studied due to its inability to detect new-coming objects. In this work, we propose TransTrack, a baseline for MOT with Transformer. It takes advantage of query-key mechanism and introduces a set of learned object queries into the pipeline to enable detecting new-coming objects. TransTrack has three main advantages: (1) It is an online joint-detection-and-tracking pipeline based on query-key mechanism. Complex and multi-step components in the previous methods are simplified. (2) It is a brand new architecture based on Transformer. The learned object query detects objects in the current frame. The object feature query from the previous frame associates those current objects with the previous ones. (3) For the first time, we demonstrate a much simple and effective method based on query-key mechanism and Transformer architecture could achieve competitive 65.8% MOTA on the MOT17 challenge dataset. We hope TransTrack can provide a new perspective for multiple-object tracking. The code is available at: <https://github.com/PeizeSun/TransTrack>.
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