A Change Detection Approach to Moving Object Detection in Low Fame-Rate Video
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.
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