Suspicious activity recognition in infrared imagery using Hidden Conditional Random Fields for outdoor perimeter surveillance

Savvas Rogotis; Dimosthenis Ioannidis; Dimitrios Tzovaras; Spiros Likothanassis

doi:10.1117/12.2182914

30 April 2015 Suspicious activity recognition in infrared imagery using Hidden Conditional Random Fields for outdoor perimeter surveillance

Savvas Rogotis, Dimosthenis Ioannidis, Dimitrios Tzovaras, Spiros Likothanassis

Author Affiliations +

Proceedings Volume 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015; 95340Q (2015) https://doi.org/10.1117/12.2182914
Event: The International Conference on Quality Control by Artificial Vision 2015, 2015, Le Creusot, France

Abstract

The aim of this work is to present a novel approach for automatic recognition of suspicious activities in outdoor perimeter surveillance systems based on infrared video processing. Through the combination of size, speed and appearance based features, like the Center-Symmetric Local Binary Patterns, short-term actions are identified and serve as input, along with user location, for modeling target activities using the theory of Hidden Conditional Random Fields. HCRFs are used to directly link a set of observations to the most appropriate activity label and as such to discriminate high risk activities (e.g. trespassing) from zero risk activities (e.g loitering outside the perimeter). Experimental results demonstrate the effectiveness of our approach in identifying suspicious activities for video surveillance systems.

Citation Download Citation

Savvas Rogotis, Dimosthenis Ioannidis, Dimitrios Tzovaras, and Spiros Likothanassis "Suspicious activity recognition in infrared imagery using Hidden Conditional Random Fields for outdoor perimeter surveillance", Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 95340Q (30 April 2015); https://doi.org/10.1117/12.2182914

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