Abstract
Identifying anomalous activities based on contextual/Scene knowledge has lots of open challenges for researchers of video analytics domain. Rule based approach to build on contextual/Scene knowledge is gaining popularity in the artificial intelligence community. Especially in the Visual Surveillance domain, adding on the rule base of contextual/Scene knowledge to the existing vision based systems would be very advantageous to make the system intelligent. Symbolizing of contextual knowledge through strong rule sets is an ongoing active research area offering a lot of options to explore and adapt. In this paper, we propose a rule-based system for intelligent monitoring of visual surveillance system taking retail domain as example. In this work we have tried to capture Contextual/Scene knowledge as a strong rule-base to fire against the annotated video input.
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Rashmi, S.R., Rangarajan, K. (2018). Rule Based Visual Surveillance System for the Retail Domain. In: Guru, D., Vasudev, T., Chethan, H., Kumar, Y. (eds) Proceedings of International Conference on Cognition and Recognition . Lecture Notes in Networks and Systems, vol 14. Springer, Singapore. https://doi.org/10.1007/978-981-10-5146-3_15
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DOI: https://doi.org/10.1007/978-981-10-5146-3_15
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