DocumentCode :
1041384
Title :
Tracking multiple humans in complex situations
Author :
Zhao, Tao ; Nevatia, Ram
Author_Institution :
Sarnoff Corp., Princeton, NJ, USA
Volume :
26
Issue :
9
fYear :
2004
Firstpage :
1208
Lastpage :
1221
Abstract :
Tracking multiple humans in complex situations is challenging. The difficulties are tackled with appropriate knowledge in the form of various models in our approach. Human motion is decomposed into its global motion and limb motion. In the first part, we show how multiple human objects are segmented and their global motions are tracked in 3D using ellipsoid human shape models. Experiments show that it successfully applies to the cases where a small number of people move together, have occlusion, and cast shadow or reflection. In the second part, we estimate the modes (e.g., walking, running, standing) of the locomotion and 3D body postures by making inference in a prior locomotion model. Camera model and ground plane assumptions provide geometric constraints in both parts. Robust results are shown on some difficult sequences.
Keywords :
cameras; image segmentation; motion estimation; object detection; object recognition; tracking; 3D body postures; camera model; ellipsoid human shape models; geometric constraints; human limb motion; image segmentation; locomotion modes estimation; multiple human object tracking; object recognition; Biological system modeling; Cameras; Ellipsoids; Humans; Legged locomotion; Reflection; Robustness; Shape; Solid modeling; Tracking; Index Terms- Multiple-human segmentation; human locomotion model.; human shape model; multiple-human tracking; visual surveillance; Algorithms; Artificial Intelligence; Computer Simulation; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Locomotion; Models, Biological; Models, Statistical; Pattern Recognition, Automated; Photography; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique; Video Recording;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2004.73
Filename :
1316854
Link To Document :
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