We present an approach to capture the 3D motion of a group of people engaged in a social interaction. The core challenges in capturing social interactions are: (1) occlusion is functional and frequent; (2) subtle motion needs to be measured over a space large enough to host a social group; (3) human appearance and configuration variation is immense; and (4) attaching markers to the body may prime the nature of interactions. The Panoptic Studio is a system organized around the thesis that social interactions should be measured through the integration of perceptual analyses over a large variety of view points. We present a modularized system designed around this principle, consisting of integrated structural, hardware, and software innovations. The system takes, as input, 480 synchronized video streams of multiple people engaged in social activities, and produces, as output, the labeled time-varying 3D structure of anatomical landmarks on individuals in the space. Our algorithm is designed to fuse the "weak" perceptual processes in the large number of views by progressively generating skeletal proposals from low-level appearance cues, and a framework for temporal refinement is also presented by associating body parts to reconstructed dense 3D trajectory stream. Our system and method are the first in reconstructing full body motion of more than five people engaged in social interactions without using markers. We also empirically demonstrate the impact of the number of views in achieving this goal.

Surveillance means is a system that monitors the behavior, activities, or other changing information, usually it is used to recognize the people for the purpose of security issues in society. This paper aims to propose a novel approach based on sensor data acquired by an optical system for the purpose of human identification. Three joints of the human body, such as the hip, knee, and ankle joint have been selected by the amount of gait movement in this algorithm. By extracting suitable 3D static and dynamic joints feature from data. The method applies the PBC (Parametric Bézier Curve) technique on the extracted features in order to derive the strong correlation between joint movements. The curve control points are used to construct the triangles of each walking pose of human. The geometric function and statistical methods on the triangles are used here to compute the gait signature and analysis of this signature and find the maximum unique relationship among the human gait during the walk, and use it as to classify the human identification. The experimental results demonstrate that this method is more accurate and reliable.