ZHU Suguo, DU Junping, REN Nan, LIANG Meiyu. Hierarchical-Based Object Detection with Improved Locality Sparse Coding[J]. Chinese Journal of Electronics, 2016, 25(2): 290-295. doi: 10.1049/cje.2016.03.015
Citation: ZHU Suguo, DU Junping, REN Nan, LIANG Meiyu. Hierarchical-Based Object Detection with Improved Locality Sparse Coding[J]. Chinese Journal of Electronics, 2016, 25(2): 290-295. doi: 10.1049/cje.2016.03.015

Hierarchical-Based Object Detection with Improved Locality Sparse Coding

doi: 10.1049/cje.2016.03.015
Funds:  This work is supported by the National Basic Research Program of China (973 Program) (No.2012CB821200, No.2012CB821206) and the National Natural Science Foundation of China (No.61320106006).
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  • Corresponding author: DU Junping (corresponding author) was born in 1963. She is now a full professor and Ph.D. supervisor in School of Computer Science and Technology, Beijing University of Posts and Telecommunications. Her research interests include artificial intelligence, image processing and pattern recognition. (Email:junpingdu@126.com)
  • Received Date: 2015-06-04
  • Rev Recd Date: 2015-07-17
  • Publish Date: 2016-03-10
  • This paper proposes to extend the hierarchical method to be adapted to sequential frames, aiming at detecting the moving object in dynamic scenes. A novel two-layer model is proposed, in which dictionaries are learned through three different stages and the locality constrained sparse representation is improved. This leads more significant improvement for performance of both static image classification and moving object detection. The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the state-of-the-art classification methods, and also able to detect moving object in the sequential frames accurately.
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