LoFSORT: Sample Online and Real-time Tracking in Low Frame Rate Scenarios

Abstract

We propose a novel motion-based tracker specifically designed for tracking multiple people in low frame rate scenarios. While previous studies have predominantly focused on scenarios with high frame rates (exceeding 10 frames per second), tracking in low frame rate conditions is significant for robotic platforms with limited computational resources. Our tracker optimizes the cost function, cascade structure and Kalman filter correction to better adapt to the characteristics of low frame rate environments. First, we enhance the cost function by incorporating stable variables through the introduction of height-based and displacement-based cost terms. Second, we prioritize handling occlusion among individuals during association, which reduces ambiguity in subsequent tracking processes. Third, we utilize the error-compensated detection to correct the Kalman filter, thereby improving tracking accuracy. Experimental results demonstrate that our proposed tracker, LoFSORT, outperforms other motion model-based trackers across various frame rate scenarios. Ablation studies further confirm that each component of our tracker enhances tracking performance in low frame rate scenarios.