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| Target Tracking Based on Enhanced Flock of Tracker and Deep Learning |
| Cheng Shuai① Cao Yong-gang①② Sun Jun-xi③ Zhao Li-rong①② Liu Guang-wen① Han Guang-liang② |
①(School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China)
②(Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130000, China)
③(School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China) |
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Abstract To solve the problem that the tracking algorithm often leads to drift and failure based on the appearance model and traditional machine learning, a tracking algorithm is proposed based on the enhanced Flock of Tracker (FoT) and deep learning under the Tracking-Learning-Detection (TLD) framework. The target is predicted and tracked by the FoT, the cascaded predictor is added to improve the precision of the local tracker based on the spatio-temporal context, and the global motion model is evaluated by the speed-up random sample consensus algorithm to improve the accuracy. A deep detector is composed of the stacked denoising autoencoder and Support Vector Machine (SVM), combines with a multi-scale scanning window with global search strategy to detect the possible targets. Each sample is weighted by the weighted P-N learning to improve the precision of the deep detector. Compared with the state-of-the-art trackers, according to the results of experiments on variant challenging image sequences in the complex environment, the proposed algorithm has more accuracy and better robust, especially for the occlusions, the background clutter and so on.
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Received: 29 October 2014
Published: 02 June 2015
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Corresponding Authors:
Sun Jun-xi
E-mail: juxi_sun@126.com
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