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Abstract

In this paper analyzed the potential of using CNNs to localize salient body movement in scenes with multiple people because such scenes are typical for vision applications in real environments. In this paper investigated a scenario that extended a typical one-person lab experiment to contain several people. One of those people performed a dynamic body gesture while the others were passive observers and only performed subtle movements. The task of the CNN was to detect and localize the person performing a gesture from an image sequence and ignore non-gesture movements. To train our network with a large data variety, to introduce an approach to combine Kinect recordings of one person into artificial scenes with multiple people, yielding a large diversity of scene configurations in our dataset. We performed experiments using these sequences and show that the proposed model is able to localize the salient body motion of gesture set. In addition a new way for detection and tracking of human full-body and body-parts with color (intensity) patch morphological segmentation and adaptive thresholding for security surveillance cameras. An adaptive threshold scheme has been developed for dealing with body size changes, illumination condition changes, and cross camera parameter changes. Tests with the PETS 2017 and 2018 datasets show that we can obtain high probability of detection and low probability of false alarm for full-body. Test results indicate that our human full -body detection method can considerably out-perform the current state -of-the - art methods in both detection performance and computational complexity

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How to Cite
RameshKumar J, Poomukilan V, & Murugan J. (2019). Improve localizing salient body motion in multi-person scenes using fuzzy with morphological segmentation algorithm. International Journal of Intellectual Advancements and Research in Engineering Computations, 7(1), 1502–1510. Retrieved from https://ijiarec.com/ijiarec/article/view/1168