Abstract
Moving Object detection based on video, of late has gained momentum in the field of research. Moving object detection has extensive application areas and is used for monitoring intelligence interaction between human and computer, transportation of intelligence, and navigating visual robotics, clarity in steering systems. It is also used in various other fields for diagnosing, compressing images, reconstructing 3D images, retrieving video images and so on. Since surveillance of human movement detection is subjective, the human objects are precisely detected to the framework proposed for human detection based on the Locomotive Object Extraction.The issue of illumination changes and crowded human image is discriminated. The image is detected through the detection feature that identifies head and shoulder and is the loci for the proposed framework. The detection of individual objects has been revamped appreciably over the recent years but even now environmental factors and crowd-scene detection remains significantly difficult for detection of moving object. The proposed framework subtracts the background through Gaussian mixture model and the area of significance is extracted. The area of significance is transformed to white and black picture by picture binarization. Then, Wiener filter is employed to scale the background level for optimizing the results of the object in motion. The object is finally identified. The performance in every stage is measured and is evaluated. The result in each stage is compared and the performance of the proposed framework is that of the existing system proves satisfactory.
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Sivasankar, C., Srinivasan, A. (2015). A Framework for Human Recognition Based on Locomotive Object Extraction. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 328. Springer, Cham. https://doi.org/10.1007/978-3-319-12012-6_47
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DOI: https://doi.org/10.1007/978-3-319-12012-6_47
Publisher Name: Springer, Cham
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