Abstract-The advancement of visual sensing has introduced better capturing of the discrete information from a complex, crowded scene for assisting in the analysis. However, after reviewing existing system, we find that majority of the work carried out till date is associated with significant problems in modeling event detection as well as reviewing abnormality of the given scene. Therefore, the proposed system introduces a model that is capable of identifying the degree of abnormality for an event captured on the crowded scene using unsupervised training methodology. The proposed system contributes to developing a novel region-wise repository to extract the contextual information about the discrete-event for a given scene. The study outcome shows highly improved the balance between the computational time and overall accuracy as compared to the majority of the standard research work emphasizing on event detection.
The proposed system highlights a novel approach of detecting and tracking multiple objects in the cluttered area like crowd using greedy algorithm. The proposed framework uses position of traced low-level feature points to generate a group of autonomous rational mobility region as resultant. Various challenging factors towards the accuracy of detection rate for multiple objects are considered. The proposed approach has detected all the feasible rational mobile regions and extorts the sub-group which increases a total likelihood function along with assignment of each traced locus to one mobile region. Performance analysis is carried out with different set of video sequences to find that proposed system has gradual robust detection rate as well as highly cost-effective computationally
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