基于CNN的粒子滤波目标跟踪算法研究

In object tracking algorithms, the target drift and missing are easy to happen in complex environments. A robust tracking algorithm was proposed based on convolutional neural network(CNN) under the particle filter framework. In this algorithm, CNN was utilized to get high-level semantic features of targets and a offline pre-train method was used to learn the general feature representation and improve the training efficiency. Using particle filter framework, the algorithm was arranged to get the reliable target motion state. In addition, two kinds of online updating procedure, long-time and short time, were introduced to deal with the target postures change and some other situations. An online hard example mining strategy was also used to improve the online learning efficiency. The simulation results show that the proposed algorithm can be effectively adapt to complex background, such as occluded, illumination changes, pose variations. Furthermore, we evaluate the proposed algorithm on some challenging videos compared with the state-of-the-art algorithms.