A biologically inspired appearance model for robust visual tracking

Shengping Zhang, Xiangyuan Lan, Hongxun Yao, Huiyu Zhou, Dacheng Tao, Xuelong Li

Research output: Contribution to journalArticlepeer-review

69 Citations (Scopus)
497 Downloads (Pure)


In this work, we propose a biologically inspired appearance model for robust visual tracking. Motivated in part by the success of the hierarchical organization of the primary visual cortex (area V1), we establish an architecture consisting of five layers: whitening, rectification, normalization, coding and polling. The first three layers stem from the models developed for object recognition. In this paper, our attention focuses on the coding and pooling layers. In particular, we use a discriminative sparse coding method in the coding layer along with spatial pyramid representation in the pooling layer, which makes it easier to distinguish the target to be tracked from its background in the presence of appearance variations. An extensive experimental study shows that the proposed method has higher tracking accuracy than several state-of-the-art trackers.
Original languageEnglish
Pages (from-to)2357-2370
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number10
Early online date19 Jul 2016
Publication statusPublished - 17 Sep 2017


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