A change detection approach to moving object detection in low fame-rate video
Reid B. Porter, Neal R. Harvey, James P. Theiler · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Moving object detection is of significant interest in temporal image analysis since it is a first step in many object identification and tracking applications. A key component in almost all moving object detection algorithms is a pixellevel classifier, where each pixel is predicted to be either part of a moving object or part of the background. In this paper we investigate a change detection approach to the pixel-level classification problem and evaluate its impact on moving object detection. The change detection approach that we investigate was previously applied to multi- and hyper-spectral datasets, where images were typically taken several days, or months apart. In this paper, we apply the approach to lowframe rate (1-2 frames per second) video datasets.