Multi-Object State Estimation using Probabilistic Belief-Based Trackers

Abstract

Agents require object-centric world models to enable Active Inference, where decisions minimize `surprise`. Maintaining high-frequency state estimation on edge hardware presents a dilemma between detection accuracy and update frequency. Traditional tracking frameworks are designed for high-frequency data and fail to bridge the large spatial uncertainty gaps that accumulate during low-frequency detection. We propose a Probabilistic Belief Tracker that decouples high-frequency belief propagation from low-frequency perception. The system utilizes a Gaussian Sum Filter with Interacting Multiple Model inspired dynamics to maintain competing motion hypotheses, representing multi-modal spatial uncertainty during detection gaps. Our results demonstrate that switching to these probabilistic beliefs provides the high-frequency continuity and identity stability required by a reliable world model. Deployment on the NVIDIA Jetson Nano confirms the architecture is viable for real-time edge deployment, while MOT17 benchmarks show that using 6x fewer detections (5~FPS) drops tracking accuracy by 10.7% and identity stability by 9.2%, relative to the 30~FPS baseline. Limiting identity switches to 172 at a 5~FPS detection rate confirms that probabilistic continuity preserves identity stability, despite the reduction in overall tracking accuracy typical of sparse detections.