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ICCV
2009
IEEE

Correlated Probabilistic Trajectories for Pedestrian Motion Detection

12 years 10 months ago
Correlated Probabilistic Trajectories for Pedestrian Motion Detection
This paper introduces an algorithm for detecting walking motion using point trajectories in video sequences. Given a number of point trajectories, we identify those which are spatio-temporally correlated as arising from feet in walking motion. Unlike existing techniques we do not assume clean point tracks but instead propose “probabilistic trajectories” as new features to classify. These are extracted from directed acyclic graphs whose edges represent temporal point correspondences and are weighted with their matching probability in terms of appearance and location. This representation tolerates the inherent trajectory ambiguity, for example due to occlusions. We then learn the correlation between the movement of two feet using a random forest classifier. The effectiveness of the algorithm is demonstrated in experiments on image sequences captured with a static camera.
Frank Perbet, Atsuto Maki, Bjorn Stenger
Added 13 Jul 2009
Updated 10 Jan 2010
Type Conference
Year 2009
Where ICCV
Authors Frank Perbet, Atsuto Maki, Bjorn Stenger
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