SIGMA: A Knowledge-Based Aerial Image Understanding System
SIGMA: A Knowledge-Based Aerial Image Understanding System
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Multiple Objects through Occlusions
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Image analysis and rule-based reasoning for a traffic monitoring system
IEEE Transactions on Intelligent Transportation Systems
Tracking multiple nonrigid objects in video sequences
IEEE Transactions on Circuits and Systems for Video Technology
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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.