| Paper: | TP-P8.5 |
| Session: | Image and Video Storage and Retrieval III |
| Time: | Tuesday, September 18, 14:30 - 17:10 |
| Presentation: |
Poster
|
| Title: |
A HMM-BASED METHOD FOR RECOGNIZING DYNAMIC VIDEO CONTENTS FROM TRAJECTORIES |
| Authors: |
Alexandre Hervieu; INRIA Rennes | | |
| | Patrick Bouthemy; INRIA Rennes | | |
| | Jean-Pierre Le Cadre; INRIA Rennes | | |
| Abstract: |
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 synthetic one and a second one formed with trajectories obtained by tracking cars in a Formula1 race video. |
