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.
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Liu, W., Xia, T., Wan, J., Zhang, Y., Li, J. (2012). RGB-D Based Multi-attribute People Search in Intelligent Visual Surveillance. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, CW., Andreopoulos, Y., Breiteneder, C. (eds) Advances in Multimedia Modeling. MMM 2012. Lecture Notes in Computer Science, vol 7131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27355-1_79
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DOI: https://doi.org/10.1007/978-3-642-27355-1_79
Publisher Name: Springer, Berlin, Heidelberg
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