Representation and optimal recognition of human activities

Towards the goal of realizing a generic automatic human activity recognition system, a new formalism is proposed. Activities are described by a chained hierarchical representation using three type of entities: image features, mobile object properties and scenarios. Taking image features of tracked moving regions from an image sequence as input, mobile object properties are first computed by specific methods while noise is suppressed by statistical methods. Scenarios are recognized from mobile object properties based on Bayesian analysis. Several scenarios are recognized by an algorithm using a probabilistic finite-state automaton (a variant of structured HMM). A demonstration of the optimality of this recognition method is discussed. Finally, the validity and the effectiveness of our approach is demonstrated on both real-world and perturbed data.

[1]  J. Aggarwal,et al.  A Bayesian approach to human activity recognition , 1999, Proceedings Second IEEE Workshop on Visual Surveillance (VS'99) (Cat. No.98-89223).

[2]  Azriel Rosenfeld,et al.  Visual surveillance and monitoring , 1998 .

[3]  Aaron F. Bobick,et al.  A Framework for Recognizing Multi-Agent Action from Visual Evidence , 1999, AAAI/IAAI.

[4]  Alex Pentland,et al.  Real-time American Sign Language recognition from video using hidden Markov models , 1995 .

[5]  Shaogang Gong,et al.  Visual Surveillance in a Dynamic and Uncertain World , 1995, Artif. Intell..

[6]  Aaron F. Bobick,et al.  Action recognition using probabilistic parsing , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[7]  Aaron F. Bobick,et al.  Recognition and interpretation of parametric gesture , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[8]  Alex Pentland,et al.  Coupled hidden Markov models for complex action recognition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.