TransTrack: Multiple Object Tracking with Transformer

2020-12-31 · Peize Sun, Jinkun Cao, Yi Jiang, Rufeng Zhang, Enze Xie, Zehuan Yuan, Changhu Wang, Ping Luo

In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an attention-based query-key mechanism. It applies object features from the previous frame as a query of the current frame and introduces a set of learned object queries to enable detecting new-coming objects. It builds up a novel joint-detection-and-tracking paradigm by accomplishing object detection and object association in a single shot, simplifying complicated multi-step settings in tracking-by-detection methods. On MOT17 and MOT20 benchmark, TransTrack achieves 74.5\% and 64.5\% MOTA, respectively, competitive to the state-of-the-art methods. We expect TransTrack to provide a novel perspective for multiple object tracking. The code is available at: \url{https://github.com/PeizeSun/TransTrack}.

📄 PDF Abstract BibTeX arXiv:2012.15460

Code (2)

PeizeSun/TransTrack 공식 구현 pytorch

ligaripash/KWtQ-face-alignment tf

Tasks

Multi-Object Tracking Multiple Object Tracking Multiple Object Tracking with TransformerObjectobject-detection Object Detection Object Tracking

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