Object Tracking Based on Deep CNN Features Together with Color Features and Sparse Representation

Abstract

To acquire the better object tracking performance in this paper, the object of interest is modeled by its RGB-color histogram feature together with deep convolutional neural network feature (deep CNN feature) and sparse representation. Because the information of the objects of interest cannot be obtained ahead in practical use, the deep CNN features should be abstracted through the pre-trained VGG network. Then the deep feature and RGB-color histogram are combined to model the object in adaptive mode. And then sparse representation is used to express the model. Above all, an adaptive particle filter algorithm based on deep CNN feature together with RGB-color feature and sparse representation is proposed to track the object of interest. Extensive experiment results demonstrate the effectiveness of our proposed method under the serious object occlusions and appearance changes.