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