A Hybrid Closed-Loop Tracker Fusing a Kalman Filter State Observer for Fast and Robust Embedded Visual Tracking

Visual object tracking finds extensive application in real-time video analysis on edge devices, yet faces dual challenges: decreased speed due to limited computational resources and weak anti-disturbance capability in complex scenarios. This paper proposes the Hybrid Closed-Loop Tracker (HCLT) to enhance both speed and robustness of embedded visual tracking. HCLT integrates high-precision and high-speed trackers to make real-time performance controllable, while a Kalman filter is employed for state observation and feedback. Within this closed-loop framework, we introduce motion and feature point information as supplementary states and further design mechanisms for adaptive search region adjustment and tracking recovery. Our methods effectively mitigate the impact of external disturbances. Experimental results demonstrate that HCLT further improves both speed and robustness on the basis of high-performance trackers, achieving high tracking accuracy across multiple public benchmark datasets. It demonstrates excellent anti-disturbance performance, particularly in challenging scenarios such as blur and occlusions, while maintaining frame rates exceeding 35 frames per second (FPS) at 720p resolution when deployed on an RK3588 embedded device, thus representing a significant improvement over deep neural network trackers.