Object tracking method based on particle filter of adaptive patches combined with multi-features fusion

Abstract

Object tracking has been one of the most important and active research areas in the field of computer vision. In this paper, we address the problem of object tracking under complex conditions in a video, which propose a object tracking method based on particle filter of adaptive patches combined color histograms with Histogram of Oriented Gradient(HOG). The adaptive patch is performed by horizontal and vertical projection based on object gray levels, which can improve the patch adaptability to the object appearance diversity and the accuracy of object tracking under occlusion conditions. The fusion of color histograms and HOG features is adopted to describe each sub-patch, which not only solves the tracking divergence problem of similar objects, but also reduces the effect of local deformation. In addition, the weighted Bhattacharyya coefficient is introduced to calculate the sub-patch matching degree of the particle, and the particle sub-patch weight will be adjusted by integrating the particle space information, and the feature model is also updated in time to achieve robust object tracking. Many simulation experiments show that our proposed algorithm achieves more favorable performance than these existing state-of-the-art algorithms in handing various challenging videos, especially occlusion and shape deformation.