Abstract
Detection counting as well as gathering features to perform analysis of behavior of natural scene is one of the complex processes to be design. The current work focuses not only to detect and count the multiple moving objects but also to understand the crowd behavior as well as exponentially reduce the issues of inter-object occlusion. The image frame sequence is considered as input for the proposed framework. Unscented Kalman filter is used for understanding the behavior of the scene as well as for increasing the detection accuracy and reducing the false positives. Designed on Matlab environment, the result shows highly accurate detection rate.
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Pushpa, D., Sheshadri, H.S. (2014). Semantic Analysis of Precise Detection Rate in Multi-Object Mobility on Natural Scene Using Kalman Filter. In: Sridhar, V., Sheshadri, H., Padma, M. (eds) Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, vol 248. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1157-0_23
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DOI: https://doi.org/10.1007/978-81-322-1157-0_23
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