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

翻译：尽管多目标追踪（MOT）近期取得进展，但遮挡、相似物体及复杂场景等障碍仍是开放性挑战。与此同时，对于流行的基于检测追踪范式在成本与性能之间的权衡问题仍缺乏系统性研究。本文提出SMILEtrack这一创新目标追踪器，通过将高效目标检测器与基于孪生网络的相似性学习模块（SLM）相结合，有效解决了上述挑战。SMILETrack的技术贡献包含两方面：首先，我们提出SLM模块，用于计算两个目标间的外观相似性，克服了分离检测与嵌入（SDE）模型中特征描述符的局限性。该模块受视觉Transformer启发，引入块级自注意力（PSA）机制，生成可靠特征以实现精准相似性匹配。其次，我们开发了相似性匹配级联（SMC）模块，其中包含新型门控函数（GATE），用于跨连续视频帧的稳健目标匹配，进一步提升MOT性能。这些创新共同帮助SMILETrack相较于多个现有最先进基准方法（包括流行的BYTETrack），在成本（如运行速度）与性能（如追踪精度）之间实现了更优权衡。在MOT17和MOT20数据集上，SMILETrack的MOTA指标高出BYTETrack 0.4-0.8个点，HOTA指标高出2.1-2.2个点。代码开源地址：https://github.com/pingyang1117/SMILEtrack_Official