Visual object tracking based on Motion-Adaptive Particle Filter under complex dynamics

Abstract This paper presents a novel particle filter called Motion-Adaptive Particle Filter (MAPF) to track fast-moving objects that have complex dynamic movements. The objective was to achieve effectiveness and robustness against abrupt motions and affine transformations. To that end, MAPF first predicted both velocity and acceleration according to prior data of the tracked objects, and then used a novel approach called sub-particle drift (SPD) to improve the dynamic model when the target made a dramatic move from one frame to the next. Finally, the propagation distances of each direction in the dynamic model were determined based on the results of motion estimation and SPD. Experimental results showed that the proposed method was robust for tracking objects with complex dynamic movements and in terms of affine transformation and occlusion. Compared to Continuously Adaptive Mean-Shift (CAM-Shift), standard particle filter (PF), Velocity-Adaptive Particle Filter (VAPF), and Memory-based Particle Filter (M-PF), the proposed tracker was superior for objects moving with large random velocities and accelerations.