Sunitha Madasi Ramach, ra, Haradagere Siddaramaiah Jayanna, Ramegowda, Journal of Information Processing Systems Vol. 15, No. 1, pp. 67-90, Feb. 2019  

https://doi.org/10.3745/JIPS.04.0100
Keywords: Consensus Based Framework, Hierarchical Graph Based Segmentation, SIFT Keypoint Descriptor
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Abstract

Accurate detection, tracking and analysis of human movement using robots and other visual surveillance systems is still a challenge. Efforts are on to make the system robust against constraints such as variation in shape, size, pose and occlusion. Traditional methods of detection used the sliding window approach which involved scanning of various sizes of windows across an image. This paper concentrates on employing a stateof- the-art, hierarchical graph based method for segmentation. It has two stages: part level segmentation for color-consistent segments and object level segmentation for category-consistent regions. The tracking phase is achieved by employing SIFT keypoint descriptor based technique in a combined matching and tracking scheme with validation phase. Localization of human region in each frame is performed by keypoints by casting votes for the center of the human detected region. As it is difficult to avoid incorrect keypoints, a consensus-based framework is used to detect voting behavior. The designed methodology is tested on the video sequences having 3 to 4 persons.

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