Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision.
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.
Department of Computer Science and Engineering, Huaqiao University, Xiamen, Fujian Province 361021, China.
Bineng
0000-0003-3423-1539
B
Zhong
Department of Computer Science and Engineering, Huaqiao University, Xiamen, Fujian Province 361021, China.
Shengnan
S
Pan
Department of Computer Science and Engineering, Huaqiao University, Xiamen, Fujian Province 361021, China.
Cheng
C
Wang
Department of Computer Science and Engineering, Huaqiao University, Xiamen, Fujian Province 361021, China.
Tian
T
Wang
Department of Computer Science and Engineering, Huaqiao University, Xiamen, Fujian Province 361021, China.
Jixiang
J
Du
Department of Computer Science and Engineering, Huaqiao University, Xiamen, Fujian Province 361021, China.
Duansheng
D
Chen
School of Information Science and Technology, Xiamen University, China.
Liujuan
L
Cao
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