Shypd.aiThe largest open index of AI toolstrackformer is an open-source Coding & Development tool that implements multi-object tracking with Transformers. It formulates the task as a frame-to-frame set prediction problem and evolves track predictions through video sequences.
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trackformer provides the official implementation of the "TrackFormer: Multi-Object Tracking with Transformers" paper. This end-to-end multi-object tracking (MOT) approach is based on an encoder-decoder Transformer architecture. It achieves data association between frames via attention, evolving track predictions through video sequences. The model initializes new tracks from static object queries and autoregressively follows existing tracks using identity-preserving track queries. Both query types leverage self- and encoder-decoder attention on global frame-level features, eliminating the need for additional graph optimization, matching, or explicit modeling of motion and appearance. TrackFormer introduces a new tracking-by-attention paradigm, delivering state-of-the-art performance on multi-object tracking (MOT17) and segmentation (MOTS20) tasks.
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