KEYWORDS: Infrared radiation, Infrared imaging, Video surveillance, Video, Surveillance, Data modeling, Detection and tracking algorithms, Quantization, Surveillance systems, Binary data
This work aims to present an extended framework for automatically recognizing suspicious activities in outdoor perimeter surveilling systems based on infrared video processing. By combining size-, speed-, and appearance-based features, like the local phase quantization and the histograms of oriented gradients, actions of small duration are recognized and used as input, along with spatial information, for modeling target activities using the theory of hidden conditional random fields (HCRFs). HCRFs are used to classify an observation sequence into the most appropriate activity label class, thus discriminating high-risk activities like trespassing from zero risk activities, such as loitering outside the perimeter. The effectiveness of this approach is demonstrated with experimental results in various scenarios that represent suspicious activities in perimeter surveillance systems.
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.
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