Multicamera People Tracking with a Probabilistic Occupancy Map Export

@article{citeulike:2402874, abstract = {Given two to four synchronized video streams taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions and lighting changes. In addition, we also derive metrically accurate trajectories for each of them. Our contribution is twofold. First, we demonstrate that our generative model can effectively handle occlusions in each time frame independently, even when the only data available comes from the output of a simple background subtraction algorithm and when the number of individuals is unknown a priori. Second, we show that multiperson tracking can be reliably achieved by processing individual trajectories separately over long sequences, provided that a reasonable heuristic is used to rank these individuals and that we avoid confusing them with one another.}, author = {Fleuret, F. and Berclaz, J. and Lengagne, R. and Fua, P.}, booktitle = {Pattern Analysis and Machine Intelligence, IEEE Transactions on}, citeulike-article-id = {2402874}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TPAMI.2007.1174}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4359319}, day = {18}, doi = {10.1109/TPAMI.2007.1174}, journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on}, keywords = {surveillance}, month = {December}, number = {2}, pages = {267--282}, posted-at = {2009-09-23 10:02:58}, priority = {2}, title = {Multicamera People Tracking with a Probabilistic Occupancy Map}, url = {http://dx.doi.org/10.1109/TPAMI.2007.1174}, volume = {30}, year = {2007} }

TY - JOUR ID - citeulike:2402874 L3 - citeulike-article-id:2402874 N2 - Given two to four synchronized video streams taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions and lighting changes. In addition, we also derive metrically accurate trajectories for each of them. Our contribution is twofold. First, we demonstrate that our generative model can effectively handle occlusions in each time frame independently, even when the only data available comes from the output of a simple background subtraction algorithm and when the number of individuals is unknown a priori. Second, we show that multiperson tracking can be reliably achieved by processing individual trajectories separately over long sequences, provided that a reasonable heuristic is used to rank these individuals and that we avoid confusing them with one another. IS - 2 JF - Pattern Analysis and Machine Intelligence, IEEE Transactions on EP - 282 TI - Multicamera People Tracking with a Probabilistic Occupancy Map VL - 30 T2 - Pattern Analysis and Machine Intelligence, IEEE Transactions on SP - 267 KW - surveillance AU - Fleuret, F AU - Berclaz, J AU - Lengagne, R AU - Fua, P PY - 2007/12/18/ UR - http://dx.doi.org/10.1109/TPAMI.2007.1174 ER -