Motion-Aware Transformer for Multi-Object Tracking

Multi-object tracking with query propagation maintains object identities by carrying track queries across frames, but the propagated state can become misaligned with the current observation before it interacts with newly initialized detection queries. We show that this temporal state misalignment can create query ownership conflicts, where a detection query represents an existing target better than its designated track-query owner. To address this problem, we introduce MATR, an end-to-end tracking framework built around a Motion-Aware Transformer (MAT) module. MAT uses current-frame evidence to pre-align both the feature and spatial states of inherited track queries before joint decoding. We further derive a geometric ambiguity measure and show empirically that ownership-conflict risk increases sharply with ambiguity, while MAT applies larger corrections to more difficult states. Across DanceTrack, SportsMOT, and BDD100K, MATR consistently improves association while introducing only modest computational overhead. On DanceTrack, MATR reaches 71.3 HOTA with 43M parameters and 179 GFLOPs. On BDD100K, without additional tracking data or external detector proposals, MATR reaches 54.7 mTETA and 41.6 mHOTA, the strongest result we are aware of among query-propagation trackers trained under this native-data setting. Source code will be publicly released upon acceptance.