Robust occlusion-aware part-based visual tracking with object scale adaptation

Visual tracking is still a challenging task as the objects suffer significant appearance changes, fast motion, and serious occlusion. In this paper, we propose an occlusion-aware part-based tracker for robust visual tracking. We first present a novel occlusion-aware part-based model based on correlation filters to integrate the global model and part-based model adaptively. It can effectively employ both the global and local information to improve the robustness of the tracker. Then we propose an integral pipeline aiming to the long-term tracking under the correlation filters, which achieves the state-of-the-art performance. In this tracking pipeline, we adopt separate translation and scale estimation. For translation estimation, we exploit and jointly learn the hierarchical features of deep Convolutional Neural Networks (CNNs) to locate the target center accurately. Then we learn an independent scale correlation filter to handle the scale variation. This design realizes scale adaptation of the target preferably, and reduces computational complexity efficiently. We further ameliorate the model update method by introducing the original reliable information. It greatly alleviates the error accumulation of the incorrect information and efficiently achieves long-term tracking. Extensive experimental results on several different challenging benchmark datasets show that our proposed tracker achieves outstanding performance against the state-of-the-art methods.