Robust object detection for intelligent surveillance systems based on radial reach correlation (RRC)

This paper describes a novel algorithm for robust object detection and segmentation, which is based on a new robust dissimilarity measure called as radial reach correlation (RRC). The capability of detecting moving objects from a complex background is one of the most fundamental technology for intelligent surveillance systems. The RRC is a new robust dissimilarity measure and has a well-formed probabilistic model of binary or normal density. The RRC evaluates the local texture between a background image and the current scene and realize robust object detection under poor conditions. To demonstrate the effectiveness of our approach, we present experimental results from real world all-directional images provided by the stereo omni-directional system (SOS).