Directional Prediction CamShift algorithm based on Adaptive Search Pattern for moving object tracking

Abstract

Moving object tracking is a fundamental task on smart video surveillance systems, because it provides a focus of attention for further investigation. Continuously Adaptive MeanShift (CamShift) algorithm is an adaptation of the MeanShift algorithm for moving objects tracking significantly, and it has been attracting increasing interests in recent years. In this work, a new CamShift approach, Directional Prediction CamShift (DP-CamShift) algorithm, is proposed to improve the tracking accuracy rate. According to the characteristic of the center-based motion vector distribution for the real-world video sequence, this work employs an Adaptive Search Pattern (ASP) to refine the central area search. The proposed approach is more robust because it adapts the optimal search pattern methods for the most adequate direction of the moving target. Since the fast Motion Estimation (ME) method has its own moving direction feature, we can adaptively use the most proper fast ME method to the certain moving object to have the best performance. Furthermore for estimation in large motion situations, the strategy of the DP-CamShift can preserve good performance. For the test video sequences with frame size of 320 × 240, the experimental results indicate that the proposed algorithm can have an accuracy rate of 99 % and achieve 23 frames per second (FPS) processing speed.