Robust online tracking via sparse gradient convolution networks

Abstract

Convolution networks trained offline have recently exhibited promising performance in object tracking tasks. However, offline training is time-consuming and their performance heavily rely on the category of auxiliary training sets. In this paper, we propose a sparse gradient convolution network without pretraining for object tracking. This approach combines shallow convolutional networks and traditional methods (gradient features and sparse representations) to avoid the offline training. In the first frame, we utilize the sparse representation method to learn a series of gradient-based local patches served as fixed filters, and they are used to convolving the input image in the subsequent frames to encode local structural information. Then, we stack all the local structure features to construct global spatial structure features, and the inner geometric layout information is preserved. Moreover, sparse coding and online updating are used to overcome issues related to target appearance variations. Qualitative and quantitative evaluations based on a challenging benchmark dataset demonstrate the effectiveness of the proposed algorithm against several state-of-the-art tracking methods.