Semantic Activity Recognition

Conference object English OPEN
Thonnat , Monique (2008)
  • Publisher: IOS Press
  • Subject: [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI] | [ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]

International audience; Extracting automatically the semantics from visual data is a real challenge. We describe in this paper how recent work in cognitive vision leads to significative results in activity recognition for visualsurveillance and video monitoring. In particular we present work performed in the domain of video understanding in our PULSAR team at INRIA in Sophia Antipolis. Our main objective is to analyse in real-time video streams captured by static video cameras and to recognize their semantic content. We present a cognitive vision approach mixing 4D computer vision techniques and activity recognition based on a priori knowledge. Applications in visualsurveillance and healthcare monitoring are shown. We conclude by current issues in cognitive vision for activity recognition.
  • References (12)
    12 references, page 1 of 2

    [1] A. Avanzi, F. Bremond, C. Tornieri, and M. Thonnat, 'Design and assessment of an intelligent activity monitoring platform', EURASIP Journal on Applied Signal Processing, special issue in ”Advances in Intelligent Vision Systems: Methods and Applications”, 2005(14), 2359- 2374, (August 2005).

    [2] B. Boulay, F. Bremond, and M. Thonnat, 'Applying 3d human model in a posture recognition system', Pattern Recognition Letter, Special Issue on vision for Crime Detection and Prevention, 27(15), 1788-1796, (2006).

    [3] Florent Fusier, Valery Valentin, Franc¸ois Bremond, Monique Thonnat, Mark Bor g, David Thirde, and James Ferryman, 'Video understanding for complex activity recognition', Machine Vision and Applications Journal, 18, 167-188, (2007).

    [4] N. Maillot and M. Thonnat, 'Ontology based complex object recognition', Image and Vision Computing Journal, Special Issue on Cognitive Computer Vision, 26(1), 102-113, (2008).

    [5] V. Martin and M. Thonnat, 'Learning contextual variations for video segmentation', in The 6th International Conference on Vision Systems (ICVW08), Santorini, Greece, (2008).

    [6] G. Medioni, I. Cohen, F. Bre´mond, S. Hongeng, and G. Nevatia, 'Activity Analysis in Video', Pattern Analysis and Machine Intelligence PAMI, 23(8), 873-889, (2001).

    [7] N. Moenne-Loccoz, F. Bre´mond, and M. Thonnat, 'Recurrent bayesian network for the recognation of human behaviors from video', in Third International Conference On Computer Vision Systems (ICVS 2003), volume LNCS 2626, pp. 44-53, Graz, Austria, (2003). Springer.

    [8] A. Toshev, F. Bre´mond, and M. Thonnat, 'An a priori-based method for frequent composite event discovery in videos', in Proceedings of 2006 IEEE International Conference on Computer Vision Systems, New York USA, (January 2006).

    [9] V-T. Vu, F. Bre´mond, and M. Thonnat, 'Automatic video interpretation: A novel algorithm for temporal scenario recognition', in The Eighteenth International Joint Conference on Artificial Intelligence (IJCAI'03), Acapulco, Mexico, (2003).

    [10] V-T. Vu, F. Bre´mond, and M. Thonnat, 'Automatic video interpretation: A recognition algorithm for temporal scenarios based on pre-compiled scenario models', in The 3rd International Conference on Vision System (ICVS'03), Graz, Austria, (2003).

  • Metrics
    No metrics available
Share - Bookmark