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ICIP
2007
IEEE

A HMM-Based Method for Recognizing Dynamic Video Contents from Trajectories

14 years 11 months ago
A HMM-Based Method for Recognizing Dynamic Video Contents from Trajectories
This paper describes an original method for classifying object motion trajectories in video sequences in order to recognize dynamic events. Similarities between trajectories are expressed from Hidden Markov Models representing each trajectory. We have favorably compared our method to several other ones, including histogram comparison, Longest Common Subsequence distance and SVM classification. Trajectory features are computed from the curvature and velocity values at each point of the trajectory, so that they are invariant to translation, rotation and scale. We have evaluated our method on two sets of data, a first one composed of typical classes of synthetic trajectories (such as parabola or clothoid), and a second one formed with trajectories obtained by tracking cars in a Formula1 race video.
Alexandre Hervieu, Patrick Bouthemy, Jean-Pierre L
Added 21 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2007
Where ICIP
Authors Alexandre Hervieu, Patrick Bouthemy, Jean-Pierre Le Cadre
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