People recognition and pose estimation in image sequences.
In: Amari, SI and Giles, CL and Gori, M and Piuri, V, (eds.)
IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL IV.
(pp. 189 - 194).
IEEE COMPUTER SOC
This paper presents a system which learns from examples to automatically recognize people and estimate their poses in image sequences with the potential application to daily surveillance in indoor environments. The person in the image is represented by a set of features based on color and shape information. Recognition is carried out through a hierarchy of biclass SVM classifiers that are separately trained to recognize people and estimate their poses. The system shows a very high accuracy in people recognition and about 85% level of performance in pose estimation, outperforming in both cases k-Nearest Neighbors classifiers. The system works in real time.
|Title:||People recognition and pose estimation in image sequences|
|Event:||IEEE/INNS/ENNS International Joint Conference on Neural Networks (IJCNN 2000)|
|Dates:||2000-07-24 - 2000-07-27|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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