HAMOT: A Hierarchical Adaptive Framework for Robust Multi-Object Tracking in Complex Environments

Multiple Object Tracking (MOT) is essential for applications such as autonomous driving, surveillance, and analytics; However, challenges such as occlusion, low-resolution imaging, and identity switches remain persistent. We propose HAMOT, a hierarchical adaptive multi-object tracker that solves these challenges with a novel, unified framework. Unlike previous methods that rely on isolated components, HAMOT incorporates a Swin Transformer-based Adaptive Enhancement (STAE) module—comprising Scene-Adaptive Transformer Enhancement and Confidence-Adaptive Feature Refinement—to improve detection under low-visibility conditions. The hierarchical Dynamic Graph Neural Network with Temporal Attention (DGNN-TA) models both short- and long-term associations, and the Adaptive Unscented Kalman Filter with Gated Recurrent Unit (AUKF-GRU) ensures accurate motion prediction. The novel Graph-Based Density-Aware Clustering (GDAC) improves occlusion recovery by adapting to scene density, preserving identity integrity. This integrated approach enables adaptive responses to complex visual scenarios, Achieving exceptional performance across all evaluation metrics, including a Higher Order Tracking Accuracy (HOTA) of 67.05%, a Multiple Object Tracking Accuracy (MOTA) of 82.4%, an ID F1 Score (IDF1) of 83.1%, and a total of 1052 Identity Switches (IDSW) on the MOT17; 66.61% HOTA, 78.3% MOTA, 82.1% IDF1, and a total of 748 IDSW on MOT20; and 66.4% HOTA, 92.32% MOTA, and 68.96% IDF1 on DanceTrack. With fixed thresholds, the full HAMOT model (all six components) achieves real-time functionality at 24 FPS on MOT17 using RTX3090, ensuring robustness and scalability for real-world MOT applications.