Enhancing multi-target tracking stability using knowledge graph integration within the Gaussian Mixture Probability Hypothesis Density Filter

Abstract

This paper proposes a novel approach to enhancing multi-target tracking of vehicles in videos with frequent camera occlusions. Our method integrates prior knowledge about vehicle behavior into a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter framework. This knowledge, extracted as a knowledge graph from historical vehicle trajectories, allows the tracker to maintain persistence even during significant interruptions. The knowledge graph models expected movement patterns and generates pseudo-observations during occlusions, similar to how time series analysis leverages historical data for forecasting. We evaluate the proposed method on both simulated and real-world video datasets using the Optimal Sub Pattern Assignment (OSPA) metric, which assesses tracking accuracy. The results show a 19.5% improvement for simulated data and a 16.5% improvement for real-world video data under fully occluded conditions, demonstrating a significant enhancement in performance.