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The ability to analyse the traffic and urban mobility pattern with the help of video analytics, which occur in massive volumes of surveillance video will lead us to provide a knowledge based for the urban planners and policy makers to come up with better construction planning. This will soothe the needs of urban commuters and thereby saving unnecessary spillage of money on the construction projects. In this research project, we present an artificial intelligence framework for transport video analytic; which autonomously models behavioural patterns of commuters and flow of traffic, wherein it taxonomically classifies essential patterns based on geometrical feature points of interest to facilitate reality mining. This behavioural pattern of commuter and traffic flow can later be queried and fetched through the newly mathematically programmed ontological data warehousing module, where such reality mined contextual data could be used for sharing essential data.
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