Computer Science ›› 2017, Vol. 44 ›› Issue (3): 182-186.doi: 10.11896/j.issn.1002-137X.2017.03.039

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Perimeter Intrusion Detection Based on Improved Convolution Neural Networks

ZHANG Yong-liang, ZHANG Zhi-qin, WU Hong-tao, DONG Ling-ping and ZHOU Bing   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Monitoring system has become one of the most important means for perimeter intrusion detection.But most of the existing monitoring systems are passive surveillance.In this paper,a method for active perimeter intrusion detection was proposed by identifying human targets in video images captured by monitoring systems.In order to enhance the robustness of different environment,this paper identified an improved convolution neural networks to realize an effective detection of human bodies with multiple postures captured by fixed cameras.Depth and shallow information are used to describe the pedestrian,so that it can improve the precision and robustness.Then,Softmax is used for classification.The experiment results confirm that the proposed algorithm has higher recognition rate for detecting human targets,which achieves recognition accuracy of 98.82% on INRIA database,99.82% on NICTA database,94.5% on CVC database and 99.92% on Daimler database,respectively.

Key words: Intelligent video analysis,Pedestrian detection,Convolution neural networks,Perimeter intrusion

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