Object tracking with adaptive motion modeling of particle filter and support vector machines

In this paper, we propose an approach for tracking arbitrary objects based on dynamic motion model and support vector machines (SVMs). When the motion of target is large or abrupt, modeling target motion is crucial for robust object tracking, however, recent advanced trackers ignore this component. In our proposed approach, we represent target motion as a random stochastic process. We use Kernelized Harmonic Means to predict the next state of target motion using few prior state vectors, and then utilize Particle filter to further optimize the predicted state. Because SVMs possess good generalization ability, while being robust against noise, we adopt online ker-nelized SVMs to the tracking problem. Our approach learns the appearance of the target during tracking, and thus the proposed method is able to adapt online to target appearance changes and its surrounding background. In addition, we incorporate our motion model within the online kernelized SVMs framework as an energy map to assign higher energy for the support vectors that are closer to the location predicted by the proposed motion model. This allows reliably maintaining smooth trajectories without unnatural jittering artifacts, which is important for long-term object tracking in the presence of occlusion and noise. Taking the dynamic model into account also improves the computational efficiency as it reduces the dense search space required for localizing the target candidate. Experimentally, we demonstrate that the proposed method outperforms state-of-the-art trackers on particularly challenging standard datasets.