Simple Human Gesture Detection and Recognition Using a Feature Vector and a Real-Time Histogram Based Algorithm

Gesture and action recognition for video surveillance is an active field of computer vision. Nowadays, there are several techniques that attempt to address this problem by 3D mapping with a high computational cost. This paper describes software algorithms that can detect the persons in the scene and analyze different actions and gestures in real time. The motivation of this paper is to create a system for thetele-assistance of elderly, which could be used as early warning monitor for anomalous events like falls or excessively long periods of inactivity. We use a method for foreg-round-background segmentation and create a feature vectorfor discriminating and tracking several people in the scene. Finally, a simple real-time histogram based algorithm is described for discriminating gestures and body positions through a K-Means clustering.

Lessons from human vision for robotic design

The visual guidance of goal-directed movements requires transformations of incoming visual information that are different from those required for visual perception.For us to grasp an object successfully,our brain must use justin-time computations of the object’s real-world size and shape,and its orientation and disposition with respect to our hand.These requirements have led to the emergence of dedicated visuomotor modules in the posterior parietal cortex of the human brain(the dorsal visual stream)that are functionally distinct from networks in the occipito-temporal cortex(the ventral visual stream)that mediate our conscious perception of the world.Although the identification and selection of goal objects and an appropriate course of action depends on the perceptual machinery of the ventral stream and associated cognitive modules,the execution of the subsequent goal-directed action is mediated by dedicated online control systems in the dorsal stream and associated motor areas.The dorsal stream allows an observer to reach out and grasp objects with exquisite ease,but by itself,deals only with objects that are visible at the moment the action is being programmed.The ventral stream,however,allows an observer to escape the present and bring to bear information from the past-including information about the function of objects,their intrinsic properties,and their location with reference to other objects in the world.Ultimately then,both streams contribute to the production of goal-directed actions.The principles underlying this division of labour between the dorsal and ventral streams are relevant to the design and implementation of autonomous robotic systems.