ACFT: adversarial correlation filter for robust tracking. Issue 14 (1st December 2019)

• Record Type: Journal Article
• Authors: Huang, Hanqiao; Zha, Yufei; Zheng, Meiyun; Zhang, Peng
• Is Part Of: IET image processing. Volume 13:Issue 14(2019)
• Page Start: 2687
• Page End: 2693
• Publication Date: 2019-12-01
• Subjects: object detection -- object tracking -- correlation methods -- filtering theory -- learning (artificial intelligence); ACFT -- robust tracking -- boundary effects -- intrinsic circular structure -- Generative Adversarial Networks -- spatial regularisation -- feature independence -- robust features -- spatial domain -- background features -- optimisation filter -- standard tracking benchmarks -- visual object tracking -- GAN; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.6672
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Tracking based on correlation filters has demonstrated outstanding performance in recent visual object tracking studies and competitions. However, the performance is limited since the boundary effects are introduced by the intrinsic circular structure. In this study, a tracker, called adversarial correlation filter tracker (ACFT), is proposed to solve the above problem through Generative Adversarial Networks (GANs) that is specifically strong at producing realistic‐looking data from noise circumstances. Especially, a mask is generated by the GANs to assist the conventional correlation filter for the spatial regularisation. By overcoming the feature independence of current regularisation in another tracker, the GANs' mask can be effectively used to identify the robust features for the target variations representation in the temporal domain. Also in the spatial domain, the background features can be substantially suppressed to obtain the optimisation filter for more reliable matching and updating. In verification, the authors evaluate the proposed tracker on the standard tracking benchmarks, and the experimental results show that their tracker outperforms favourably against other state‐of‐the‐art trackers in the measurements of accuracy and robustness.