Abstract
Since human face is often not clear in the surveillance video, this paper proposes a retrieval method on the whole body with fine-grained feature extraction. This method first extracts foreground region of human movement based on Gaussian mixed model (GMM). The human body is divided into two parts, head and below the head, based on the human morphological features and skin color. There are three parts coupled with the whole body, and then, we extract color feature for each part. Secondly, the human body is divided into front and back feature samples according to the a priori knowledge. Calculate the gap between the retrieval eigenvectors and the eigenvectors of the target body, then determine whether match. The experimental results show that this method maintains better retrieval precision when the recall rate is high. This paper target retrieve is real time on video.
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Acknowledgment
The authors would like to thank our anonymous reviewers for their valuable comments. This work was supported in part by grants from National Natural Science Foundation of China (Nos. 61303101, 61170326, and 61170077), the Natural Science Foundation of Guangdong Province, China (No. S2012040008028 and S2013010012555), the Shenzhen Research Foundation for Basic Research, China (Nos. JCYJ20120613170718514, JCYJ20130326112201234, JC201005250052A, JC20130325014346, JCYJ20130329102051856, and ZD201010250104A), the Shenzhen Peacock Plan (No. KQCX20130621101205783), the Start-up Research Foundation of Shenzhen University (Nos. 2012-801 and 2013-000009), and Shenzhen Nanshan District entrepreneurship research (308298210022).
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Wen, Z., Gao, J., Liu, F., Wu, H. (2014). A Multigranularity Surveillance Video Retrieval Algorithm for Human Targets. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_16
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DOI: https://doi.org/10.1007/978-3-642-54924-3_16
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