A Reliable Feature-Based Framework for Vehicle Tracking in Advanced Driver Assistance Systems

Vehicle tracking has always been a vital aspect of modern transportation systems. This phenomenon has gained even more interest with the introduction of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles. Most state-of-the-art (SOTA) vehicle trackers, and their enhanced versions, commonly rely on mathematical motion models (e.g., Kalman Filter) as the core information. However, these models may produce unreliable outputs, especially when objects exhibit complex motion patterns. Hence, we propose a reliable feature-based tracking framework that fully exploits distinct vehicle appearance and conduct a comparative analysis with classic motion-based trackers. Additionally, we revisit previously proposed track handling strategies to incorporate a specially designed track management system for feature-based tracking. The proposed method achieves the highest score on all selected multi-object-tracking (MOT) evaluation metrics compared to the current SOTA methods on the KITTI dataset. Notably, our approach experienced significantly low False Positive (FP) errors, ensuring its performance in minimizing unreliable information.