Abstract
We integrate human detection and regional affine invariant feature tracking into a robust human tracking system. First, foreground blobs are detected using background subtraction. The background model is built with a local predictive model to cope with large illumination changes. Detected foreground blobs are then used by a box tracker to establish stable tracks of moving objects. Human detection hypotheses are detected using a combination of both shape and region information through a hierarchical part-template matching method. Human detection results are then used to refine tracks for moving people. Track refinement, extension and merging are carried out with a robust tracker that is based on regional affine invariant features. We show experimental results for the separate components as well as the entire system.
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Tran, S., Lin, Z., Harwood, D., Davis, L. (2008). UMD_VDT, an Integration of Detection and Tracking Methods for Multiple Human Tracking. In: Stiefelhagen, R., Bowers, R., Fiscus, J. (eds) Multimodal Technologies for Perception of Humans. RT CLEAR 2007 2007. Lecture Notes in Computer Science, vol 4625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68585-2_15
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DOI: https://doi.org/10.1007/978-3-540-68585-2_15
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