Abstract
The authors present a simple but robust real-time algorithm that allows tracking of multiple objects in complex environments. As the first stage, the foreground segmentation uses luminance contrast, reducing computation time avoiding the use colour information at this stage. Foreground pixels are then grouped into blobs analysing X-Y histograms. Tracking is achieved by matching blobs from two consecutive frames using overlapping information from bounding boxes and a linear prediction for the centroid’s position. This method successfully solves blobs merging into groups and tracking them until they split again. Application in automatic surveillance is suggested by linking blob’s information, in terms of trajectories and positions, with the events to be detected. Some examples in transport environments are outlined.
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Fuentes, L.M., Velastin, S.A. (2003). Tracking People for Automatic Surveillance Applications. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_28
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DOI: https://doi.org/10.1007/978-3-540-44871-6_28
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