Abstract
Surveillance video is characterized by large amount of data and redundancy, which makes the suspicious face detection to be a problem. To solve the problem above we proposed suspicious face detection based on key frame. Surveillance video has the type of fixed background, so this paper used the frame difference method with low computational complexity and small computation to extract the key frame. We proposed a new method combined DPM with skin color detection to detect the suspicious face in the key frames. To solve the speed bottleneck of the traditional DPM, we proposed to use the fast HOG LUT feature extraction and the near optimal cost sensitive decision making improving the traditional method. Meanwhile we used YCrCb + otsu skin color segmentation. Since the otsu is easily affected by the illumination, we proposed an improved skin color detection. Experiment results show that the proposed algorithms are robust and accurate for real-time.
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Acknowledgments
This research work is supported by the grant of Guangxi science and technology development project (No: 1598018-6), the grant of Guangxi Experiment Center of Information Science of Guilin University of Electronic Technology, the grant of Guangxi Colleges and Universities Key Laboratory of cloud computing and complex systems of Guilin University of Electronic Technology (No: 15210), the grant of Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics of Guilin University of Electronic Technology (No: GIIP201403), the grant of Guangxi Key Laboratory of Trusted Software of Guilin University of Electronic Technology(No: KX201513).
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Zheng, X., Ning, Y., Chen, X., Zhan, Y. (2016). Suspicious Face Detection Based on Key Frame Recognition Under Surveillance Video. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_65
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DOI: https://doi.org/10.1007/978-3-319-41000-5_65
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