Detecting Salient Motion by Accumulating Directionally-Consistent Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Understanding transit scenes: a survey on human behavior-recognition algorithms
IEEE Transactions on Intelligent Transportation Systems
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We present a new approach to the tracking of very non rigid patterns of motion, such as water flowing down a stream. The algorithm is based on a "disturbance map," which is obtained by linearly subtracting the temporal average of the previous frames from the new frame. Every local motion creates a disturbance having the form of a wave, with a "head" at the present position of the motion and a historical "tail" that indicates the previous locations of that motion. These disturbances serve as loci of attraction for "tracking particles" that are scattered throughout the image. The algorithm is very fast and can be performed in real time. We provide excellent tracking results on various complex sequences, using both stabilized and moving cameras, showing: a busy ant column, waterfalls, rapids and flowing streams, shoppers in a mall, and cars in a traffic intersection.