Vision based Traffic Police Hand Signal Recognition in Surveillance Video- A Survey R.Sathya <s> Body Model. <s> This paper reports the first phase of a research program on visual perception of motion patterns characteristic of living organisms in locomotion. Such motion patterns in animals and men are termed here as biological motion. They are characterized by a far higher degree of complexity than the patterns of simple mechanical motions usually studied in our laboratories. In everyday perceptions, the visual information from biological motion and from the corresponding figurative contour patterns (the shape of the body) are intermingled. A method for studying information from the motion pattern per se without interference with the form aspect was devised. In short, the motion of the living body was represented by a few bright spots describing the motions of the main joints. It is found that 10–12 such elements in adequate motion combinations in proximal stimulus evoke a compelling impression of human walking, running, dancing, etc. The kinetic-geometric model for visual vector analysis originally developed in the study of perception of motion combinations of the mechanical type was applied to these biological motion patterns. The validity of this model in the present context was experimentally tested and the results turned out to be highly positive. <s> BIB001 </s> Vision based Traffic Police Hand Signal Recognition in Surveillance Video- A Survey R.Sathya <s> Body Model. <s> Action recognition has become a very important topic in computer vision, with many fundamental applications, in robotics, video surveillance, human-computer interaction, and multimedia retrieval among others and a large variety of approaches have been described. The purpose of this survey is to give an overview and categorization of the approaches used. We concentrate on approaches that aim on classification of full-body motions, such as kicking, punching, and waving, and we categorize them according to how they represent the spatial and temporal structure of actions; how they segment actions from an input stream of visual data; and how they learn a view-invariant representation of actions. <s> BIB002
Represent the spatial structure of the human body. Video streams are observed each frame, the pose of human body is recovered from a variety of available features. BIB001 human action recognition is performed based on such pose estimates. Recognition divides the task of action recognition in two separate stages. A motion captures stage which estimates a 3D model of the human body. BIB002 human body typically represented as a kinematic joint model and an action recognition stage which operates on joint trajectories.