Study on self organizing approach for moving object detection and tracking for visual surveillance
Neha Dhavale · 2013
Visual surveillance is a very active research area in computer vision and moving object detection and tracking is often the first step in applications such as video surveillance. Here, we propose a methodology to detect and track human image based on self organization through artificial neural networks. We choose the HSV color space, relying on the hue, saturation and value properties of each color to represent each weight vector. A neural network mapping method is proposed to use a whole trajectory incrementally in time fed as an input to the network. The adopted artificial neural network is organized as a 2-D flat grid of neurons (or nodes). Each node computes a function of the weighted linear combination of incoming inputs, where weights resemble the neural network learning. Each node could be represented by a weight vector obtained ,collecting the weights related to incoming links. An incoming pattern is mapped to the node whose model is most similar (according to a predefined metric) to the pattern, and weight vectors in a neighborhood of such node are updated. We would focus on combining contour projection analysis with shape analysis to remove the shadow effect.