Abstract
We present a system, which is able to track multiple objects under partial and total occlusion. The reasoning system builds up a graph based spatio-temporal representation of object hypotheses and thus is able to explain the scene even if objects are totally occluded. Furthermore it adapts the object models and learns new appearances at assumed object locations. We represent objects in a star-shaped geometrical model of interest points using a codebook. The novelty of our system is to combine a spatio-temporal reasoning system and an interest point based object detector for on-line improving of object models in terms of adding new, and deleting unreliable interest points. We propose this system for a consistent representation of objects in an image sequence and for learning changes of appearances on the fly.
The work described in this article has been funded by European projects under the contract no. 6029427, no. 215821 and no. 216886, as well as by the Austrian Science Foundation under the grant #S9101 and #S9104 (“Cognitive Vision”).
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Prankl, J., Antenreiter, M., Auer, P., Vincze, M. (2009). Consistent Interpretation of Image Sequences to Improve Object Models on the Fly . In: Fritz, M., Schiele, B., Piater, J.H. (eds) Computer Vision Systems. ICVS 2009. Lecture Notes in Computer Science, vol 5815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04667-4_39
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DOI: https://doi.org/10.1007/978-3-642-04667-4_39
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