Object tracking based on Kalman particle filter with LSSVR

In visual tracking field, traditional Kalman particle filter often suffers from the accuracy loss when estimating the target. To alleviate this problem, we propose a novel object tracking method with the fusion of the extended Kalman particle filter (EKPF) and the least squares support vector regression (LSSVR). First, the observation value of Kalman filter is acquired with the cues of color and motion features. The importance probability density function is generated by extended Kalman filter (EKF), which makes the distribution of particles approximately to the posterior probability. And then, a weighted plan is used to determine the weighted coefficient of LSSVR model, the robustness and sparseness of LSSVR modeling will thereby be enhanced. The updated EKF features of tested samples served as training samples to establish the dynamic LSSVR model real-time in the next frame. Finally, the LSSVR is used to calibrate the tracking results of Kalman particle filter, such that the tracking object will always follow the correct motion trajectory. The experimental results show that our method performs favorably against traditional Kalman particle filter with real-time performance and strong robustness.