Strike a pose: tracking people by finding stylized poses

• Published in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 271 - 278 vol. 1
• Main Authors: Ramanan, D., Forsyth, D.A., Zisserman, A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Data mining; Detectors; Humans; Kinematics; Large-scale systems; Legged locomotion; Predictive models; Surveillance; Testing; Tracking
• DOI: 10.1109/CVPR.2005.335
• ISBN: 0769523722; 9780769523729
• ISSN: 1063-6919

We develop an algorithm for finding and kinematically tracking multiple people in long sequences. Our basic assumption is that people tend to take on certain canonical poses, even when performing unusual activities like throwing a baseball or figure skating. We build a person detector that quite accurately detects and localizes limbs of people in lateral walking poses. We use the estimated limbs from a detection to build a discriminative appearance model; we assume the features that discriminate a figure in one frame will discriminate the figure in other frames. We then use the models as limb detectors in a pictorial structure framework, detecting figures in unrestricted poses in both previous and successive frames. We have run our tracker on hundreds of thousands of frames, and present and apply a methodology for evaluating tracking on such a large scale. We test our tracker on real sequences including a feature-length film, an hour of footage from a public park, and various sports sequences. We find that we can quite accurately automatically find and track multiple people interacting with each other while performing fast and unusual motions.