Trajectory Rectification and Path Modeling for Video Surveillance Export

@inproceedings{citeulike:5925699, abstract = {Path modeling for video surveillance is an active area of research. We address the issue of Euclidean path modeling in a single camera for activity monitoring in a multi- camera video surveillance system. The paper proposes (i) a novel linear solution to auto-calibrate any camera observing pedestrians and (ii) to use these calibrated cameras to detect unusual object behavior. During the unsupervised training phase, after auto-calibrating a camera and metric rectifying the input trajectories, the input sequences are registered to the satellite imagery and prototype path models are constructed. This allows us to estimate metric information directly from the video sequences. During the testing phase, using our simple yet efficient similarity measures, we seek a relation between the input trajectories derived from a sequence and the prototype path models. We test the proposed method on synthetic as well as on real-world pedestrian sequences.}, author = {Junejo, Imran N. and Foroosh, Hassan}, booktitle = {2007 IEEE 11th International Conference on Computer Vision}, citeulike-article-id = {5925699}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/ICCV.2007.4409008}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4409008}, day = {21}, doi = {10.1109/ICCV.2007.4409008}, isbn = {978-1-4244-1630-1}, issn = {1550-5499}, keywords = {crowdreview, geography, path, spatial, spatial\_regions, surveillance, tracking}, location = {Rio de Janeiro, Brazil}, month = {October}, pages = {1--7}, posted-at = {2009-12-10 13:47:48}, priority = {2}, publisher = {IEEE}, title = {Trajectory Rectification and Path Modeling for Video Surveillance}, url = {http://dx.doi.org/10.1109/ICCV.2007.4409008}, year = {2007} }

TY - CONF ID - citeulike:5925699 L3 - citeulike-article-id:5925699 N2 - Path modeling for video surveillance is an active area of research. We address the issue of Euclidean path modeling in a single camera for activity monitoring in a multi- camera video surveillance system. The paper proposes (i) a novel linear solution to auto-calibrate any camera observing pedestrians and (ii) to use these calibrated cameras to detect unusual object behavior. During the unsupervised training phase, after auto-calibrating a camera and metric rectifying the input trajectories, the input sequences are registered to the satellite imagery and prototype path models are constructed. This allows us to estimate metric information directly from the video sequences. During the testing phase, using our simple yet efficient similarity measures, we seek a relation between the input trajectories derived from a sequence and the prototype path models. We test the proposed method on synthetic as well as on real-world pedestrian sequences. CY - Rio de Janeiro, Brazil SN - 978-1-4244-1630-1 EP - 7 TI - Trajectory Rectification and Path Modeling for Video Surveillance PB - IEEE T2 - 2007 IEEE 11th International Conference on Computer Vision SP - 1 KW - crowdreview KW - geography KW - path KW - spatial KW - spatial_regions KW - surveillance KW - tracking AU - Junejo, Imran AU - Foroosh, Hassan PY - 2007/10/21/ UR - http://dx.doi.org/10.1109/ICCV.2007.4409008 ER -