Human posture recognition using active contours and radial basis function neural network

In this paper an automated video surveillance system for human posture recognition using active contours and neural networks is presented. Localization of moving objects in the scene and human posture estimation are key features of the proposed architecture. The system architecture consists of five sequential modules that include the moving target detection process, two levels of segmentation process for interested element localization, features extraction of the object shape and a human posture classification system based on the radial basis functions neural network. Moving objects are detected by using an adaptive background subtraction method with an automatic background adaptation speed parameter and a new fast gradient vector flow snake algorithm for the elements segmentation is proposed. The developed system has been tested for the classification of three different postures such as standing, bending and squatting considering different kinds of feature. Results are promising and the architecture is also useful for the discrimination of human activities.