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Despite recent progress in Multiple Object Tracking (MOT), several obstaclessuch as occlusions, similar objects, and complex scenes remain an openchallenge. Meanwhile, a systematic study of the cost-performance tradeoff forthe popular tracking-by-detection paradigm is still lacking. This paperintroduces SMILEtrack, an innovative object tracker that effectively addressesthese challenges by integrating an efficient object detector with a Siamesenetwork-based Similarity Learning Module (SLM). The technical contributions ofSMILETrack are twofold. First, we propose an SLM that calculates the appearancesimilarity between two objects, overcoming the limitations of featuredescriptors in Separate Detection and Embedding (SDE) models. The SLMincorporates a Patch Self-Attention (PSA) block inspired by the visionTransformer, which generates reliable features for accurate similaritymatching. Second, we develop a Similarity Matching Cascade (SMC) module with anovel GATE function for robust object matching across consecutive video frames,further enhancing MOT performance. Together, these innovations help SMILETrackachieve an improved trade-off between the cost ({\em e.g.}, running speed) andperformance (e.g., tracking accuracy) over several existing state-of-the-artbenchmarks, including the popular BYTETrack method. SMILETrack outperformsBYTETrack 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

SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking