Papers › SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking

SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking

16 Nov 2022arXiv:2211.08824archive 2025-07-28

Yu-Hsiang Wang, Jun-Wei Hsieh, Ping-Yang Chen, Ming-Ching Chang, Hung Hin So, Xin Li

Despite recent progress in Multiple Object Tracking (MOT), several obstacles such as occlusions, similar objects, and complex scenes remain an open challenge. Meanwhile, a systematic study of the cost-performance tradeoff for the popular tracking-by-detection paradigm is still lacking. This paper introduces SMILEtrack, an innovative object tracker that effectively addresses these challenges by integrating an efficient object detector with a Siamese network-based Similarity Learning Module (SLM). The technical contributions of SMILETrack are twofold. First, we propose an SLM that calculates the appearance similarity between two objects, overcoming the limitations of feature descriptors in Separate Detection and Embedding (SDE) models. The SLM incorporates a Patch Self-Attention (PSA) block inspired by the vision Transformer, which generates reliable features for accurate similarity matching. Second, we develop a Similarity Matching Cascade (SMC) module with a novel GATE function for robust object matching across consecutive video frames, further enhancing MOT performance. Together, these innovations help SMILETrack achieve an improved trade-off between the cost ({\em e.g.}, running speed) and performance (e.g., tracking accuracy) over several existing state-of-the-art benchmarks, including the popular BYTETrack method. SMILETrack outperforms BYTETrack by 0.4-0.8 MOTA and 2.1-2.2 HOTA points on MOT17 and MOT20 datasets. Code is available at https://github.com/pingyang1117/SMILEtrack_Official

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Code

pingyang1117/smiletrack_official officialmentioned in paperpytorch report

WWangYuHsiang/SMILEtrack mentioned on GitHubpytorch report

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Tasks

Multi-Object Tracking Multiple Object Tracking Object Object Detection Object Tracking object-detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Multi-Object Tracking	MOT17	SMILEtrack	HOTA	65.24	#7 of 48	Archive leaderboard	report
Multi-Object Tracking	MOT17	SMILEtrack	IDF1	80.5	#7 of 48	Archive leaderboard	report
Multi-Object Tracking	MOT17	SMILEtrack	MOTA	81.06	#7 of 48	Archive leaderboard	report
Multi-Object Tracking	MOT20	SMILEtrack	HOTA	63.4	#7 of 27	Archive leaderboard	report
Multi-Object Tracking	MOT20	SMILEtrack	IDF1	77.5	#7 of 27	Archive leaderboard	report
Multi-Object Tracking	MOT20	SMILEtrack	MOTA	78.2	#7 of 27	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Siamese Network Softmax Test Transformer

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