Improvement of a Temporal Video Index Produced by an Object Detector

The goal of the works described in this paper is to improve results produced by an object detector operating independently on each frame of a video document in order to generate a more robust index. Results of the object detector are “smoothed” along the time dimension using a temporal window. For a given frame, we count the number of occurrences of each object in the previous and next frames, and then only the objects whose number of appearance is above a threshold are validated. In this paper, we present a probabilistic approach for theoretically computing these thresholds. This approach is well suited to limit the number of false alarms provided by the static detector, and its principle of detection generalization also allows some detections that can be missed by the detector.

[1]  Alain Crouzil,et al.  Non-rigid object localization from color model using mean shift , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[2]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Cordelia Schmid,et al.  Face detection in a video sequence - a temporal approach , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[4]  Tno Tpd.  TRECVID 2004 - An Introduction , 2004 .

[5]  G. Jaffré,et al.  Costume: a new feature for automatic video content indexing , 2004 .

[6]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[7]  Feller William,et al.  An Introduction To Probability Theory And Its Applications , 1950 .

[8]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[9]  Stéphane Marchand-Maillet,et al.  Towards a Standard Protocol for the Evaluation of Video-to-Shots Segmentation Algorithms , 1999 .

[10]  Andrew Zisserman,et al.  Efficient object retrieval from videos , 2004, 2004 12th European Signal Processing Conference.

[11]  Marcel Worring,et al.  Multimodal Video Indexing : A Review of the State-ofthe-art , 2001 .