Robust occlusion-aware part-based visual tracking with object scale adaptation. (September 2018)

• Record Type: Journal Article
• Authors: Wang, Xin; Hou, Zhiqiang; Yu, Wangsheng; Pu, Lei; Jin, Zefenfen; Qin, Xianxiang
• Is Part Of: Pattern recognition. Volume 81(2018:Sep.)
• Page Start: 456
• Page End: 470
• Publication Date: 2018-09
• Subjects: Visual tracking -- Correlation filters -- Convolutional neural networks -- Object occlusion -- Online model update; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.04.011
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A novel occlusion-aware part-based model is proposed. A new model update method which maintains the long-term memory of target appearance is improved. An integral pipeline aiming to the long-term tracking is proposed under the correlation filters. Satisfactory performance of our tracker is achieved on several challenging datasets. Abstract: 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.