Fast tracking using edge histograms

This paper proposes a new algorithm for tracking objects and objects boundaries. This algorithm was developed and applied in a system used for compositing computer generated images and real world video sequences, but can be applied in general in all tracking systems where accuracy and high processing speed are required. The algorithm is based on analysis of histograms obtained by summing along chosen axles (for example, along respectively horizontal and vertical axles) pixels of edge segmented images. Edge segmentation is done by spatial convolution using gradient operator. The advantage of such an approach is that it can be performed in real-time using available on the market hardware convolution filters (such filters are, for example, built in and ready available in SGI's Onyx RealityEngine2). After edge extraction and histograms computation, respective positions of maximums in edge intensity histograms, in current and previous frame, are compared and matched. Obtained this way information about displacement of histograms maximums, can be directly converted (using correlation and interpolation) into information about changes of target boundaries positions along chosen axles.