RGB-D based multi-attribute people search in intelligent visual surveillance

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Abstract

Searching people in surveillance videos is a typical task in intelligent visual surveillance (IVS). However, current IVS techniques can hardly handle multi-attribute queries, which is a natural way of finding people in real-world. The challenges arise from the extraction of multiple attributes which largely suffer from illumination change, shadow and complicated background in the real-world surveillance environments. In this paper, we investigate how these challenges can be addressed when IVS is equipped with RGB-D information obtained by an RGB-D camera. With the RGB-D information, we propose methods that accurately and robustly segment human region and extract three groups of attributes including biometrical attributes, appearance attributes and motion attributes. Furthermore, we introduce a novel IVS system which is capable of handling multi-attribute queries for searching people in surveillance videos. Experimental evaluations demonstrate the effectiveness of the proposed method and system, and also the promising applications of bringing RGB-D information into IVS. © 2012 Springer-Verlag.

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APA

Liu, W., Xia, T., Wan, J., Zhang, Y., & Li, J. (2012). RGB-D based multi-attribute people search in intelligent visual surveillance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7131 LNCS, pp. 750–760). https://doi.org/10.1007/978-3-642-27355-1_79

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