iSurveillance: Intelligent framework for multiple events detection in surveillance videos

• It is essential to automate the video surveillance systems due to human limitation.
• Based on the principle of compositionality, this paper proposed the iSurveillance.
• Our work shows good results in detecting multiple events under a unified framework.
• Our work is opposed to prior methods that are tailor-made and designed to work in specific area.
• Future work focus on new domain knowledge, dataset and improve variables complexity.

Research in the video surveillance is gaining more popularity due to its widespread applications as well as social impact. In this paper, we present an intelligent framework for detection of multiple events in surveillance videos. Based on the principle of compositionality, we modularize the surveillance problems into a set of variables comprising regions-of-interest, classes (i.e. human, vehicle), attributes (i.e. speed, locality) and a set of notions (i.e. rules) associated to each of the attributes to construct a knowledge-based understanding of the environment. The final output from the reasoning process, which combines the definition domains of the various variables, allows a broader and integrated understanding of complex pattern of activities in the scene. This is in contrast to the state-of-the-art solutions that are only able to perform only a singular task, at a time. Experimental results on both the public and real-time datasets have demonstrated the effectiveness and robustness of the proposed framework in detecting multiple events in surveillance videos.