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http://dx.doi.org/10.9717/kmms.2017.20.2.126

Object Tracking using Feature Map from Convolutional Neural Network  

Lim, Suchang (Dept. of Computer Science, Sunchon National University)
Kim, Do Yeon (Dept. of Computer Engineering, Sunchon National University)
Publication Information
Abstract
The conventional hand-crafted features used to track objects have limitations in object representation. Convolutional neural networks, which show good performance results in various areas of computer vision, are emerging as new ways to break through the limitations of feature extraction. CNN extracts the features of the image through layers of multiple layers, and learns the kernel used for feature extraction by itself. In this paper, we use the feature map extracted from the convolution layer of the convolution neural network to create an outline model of the object and use it for tracking. We propose a method to adaptively update the outline model to cope with various environment change factors affecting the tracking performance. The proposed algorithm evaluated the validity test based on the 11 environmental change attributes of the CVPR2013 tracking benchmark and showed excellent results in six attributes.
Keywords
Object Tracking; Correlation Filter; CNN; Feature Map; Appearance Model;
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Times Cited By KSCI : 1  (Citation Analysis)
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