Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision. (25th August 2016)

• Record Type: Journal Article
• Authors: Zhong, Bineng; Pan, Shengnan; Wang, Cheng; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan
• Other Names: Ma Qin Academic Editor.
• Is Part Of: BioMed research international. Volume 2016(2016)
• Publication Date: 2016-08-25
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2016/8182416
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : Tracking individual-cell/object over time is important in understanding drug treatment effects on cancer cells and video surveillance. A fundamental problem of individual-cell/object tracking is to simultaneously address the cell/object appearance variations caused by intrinsic and extrinsic factors. In this paper, inspired by the architecture of deep learning, we propose a robust feature learning method for constructing discriminative appearance models without large-scale pretraining. Specifically, in the initial frames, an unsupervised method is firstly used to learn the abstract feature of a target by exploiting both classic principal component analysis (PCA) algorithms with recent deep learning representation architectures. We use learned PCA eigenvectors as filters and develop a novel algorithm to represent a target by composing of a PCA-based filter bank layer, a nonlinear layer, and a patch-based pooling layer, respectively. Then, based on the feature representation, a neural network with one hidden layer is trained in a supervised mode to construct a discriminative appearance model. Finally, to alleviate the tracker drifting problem, a sample update scheme is carefully designed to keep track of the most representative and diverse samples during tracking. We test the proposed tracking method on two standard individual cell/object tracking benchmarks to show our tracker's state-of-the-art performance.