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.
|Number of pages||14|
|Journal||IEEE Transactions on Neural Networks and Learning Systems|
|Early online date||19 Jul 2016|
|Publication status||Published - 17 Sep 2017|
Zhang, S., Lan, X., Yao, H., Zhou, H., Tao, D., & Li, X. (2017). A biologically inspired appearance model for robust visual tracking. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2357-2370. https://doi.org/10.1109/TNNLS.2016.2586194