Multi-Object Tracking (MOT) represents a fundamental but computationally demanding task in computer vision, with particular challenges arising in occluded and densely populated environments. While contemporary tracking systems have demonstrated considerable progress, persistent limitations—notably frequent occlusion-induced identity switches and tracking inaccuracies—continue to impede reliable real-world deployment. This work introduces an advanced tracking framework that enhances association robustness through a two-stage matching paradigm combining spatial and appearance features. Proposed framework employs: (1) a Height Modulated and Scale Adaptive Spatial Intersection-over-Union (HMSIoU) metric for improved spatial correspondence estimation across variable object scales and partial occlusions; (2) a feature extraction module generating discriminative appearance descriptors for identity maintenance; and (3) a recovery association mechanism for refining matches between unassociated tracks and detections. Comprehensive evaluation on standard MOT17 and MOT20 benchmarks demonstrates significant improvements in tracking consistency, with state-of-the-art performance across key metrics including HOTA (64), MOTA (80.7), IDF1 (79.8), and IDs (1379). These results substantiate the efficacy of our Cue-Tracker framework in complex real-world scenarios characterized by occlusions and crowd interactions.