Abstract
We describe a tracker that can track moving people in long sequences without manual initialization. Moving people are modeled with the assumption that, while configuration can vary quite substantially from frame to frame, appearance does not. This leads to an algorithm that firstly builds a model of the appearance of the body of each individual by clustering candidate body segments, and then uses this model to find all individuals in each frame. Unusually, the tracker does not rely on a model of human dynamics to identify possible instances of people; such models are unreliable, because human motion is fast and large accelerations are common. We show our tracking algorithm can be interpreted as a loopy inference procedure on an underlying Bayes net. Experiments on video of real scenes demonstrate that this tracker can (a) count distinct individuals; (b) identify and track them; (c) recover when it loses track, for example, if individuals are occluded or briefly leave the view; (d) identify the configuration of the body largely correctly; and (e) is not dependent on particular models of human motion.
| Original language | English (US) |
|---|---|
| Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
| Volume | 2 |
| State | Published - Sep 1 2003 |
| Externally published | Yes |
| Event | 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Madison, WI, United States Duration: Jun 18 2003 → Jun 20 2003 |
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ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
Cite this
Finding and tracking people from the bottom up. / Ramanan, Deva; Forsyth, David Alexander.
In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, 01.09.2003.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Finding and tracking people from the bottom up
AU - Ramanan, Deva
AU - Forsyth, David Alexander
PY - 2003/9/1
Y1 - 2003/9/1
N2 - We describe a tracker that can track moving people in long sequences without manual initialization. Moving people are modeled with the assumption that, while configuration can vary quite substantially from frame to frame, appearance does not. This leads to an algorithm that firstly builds a model of the appearance of the body of each individual by clustering candidate body segments, and then uses this model to find all individuals in each frame. Unusually, the tracker does not rely on a model of human dynamics to identify possible instances of people; such models are unreliable, because human motion is fast and large accelerations are common. We show our tracking algorithm can be interpreted as a loopy inference procedure on an underlying Bayes net. Experiments on video of real scenes demonstrate that this tracker can (a) count distinct individuals; (b) identify and track them; (c) recover when it loses track, for example, if individuals are occluded or briefly leave the view; (d) identify the configuration of the body largely correctly; and (e) is not dependent on particular models of human motion.
AB - We describe a tracker that can track moving people in long sequences without manual initialization. Moving people are modeled with the assumption that, while configuration can vary quite substantially from frame to frame, appearance does not. This leads to an algorithm that firstly builds a model of the appearance of the body of each individual by clustering candidate body segments, and then uses this model to find all individuals in each frame. Unusually, the tracker does not rely on a model of human dynamics to identify possible instances of people; such models are unreliable, because human motion is fast and large accelerations are common. We show our tracking algorithm can be interpreted as a loopy inference procedure on an underlying Bayes net. Experiments on video of real scenes demonstrate that this tracker can (a) count distinct individuals; (b) identify and track them; (c) recover when it loses track, for example, if individuals are occluded or briefly leave the view; (d) identify the configuration of the body largely correctly; and (e) is not dependent on particular models of human motion.
UR - http://www.scopus.com/inward/record.url?scp=0041940183&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0041940183&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:0041940183
VL - 2
JO - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
JF - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SN - 1063-6919
ER -