Group Behavior Recognition for Gesture Analysis

This paper analyzes the movements of the human body limbs (hands, feet and head) and center of gravity in order to detect and analyze simple actions such as walking and running. We propose a novel vision of the human body, by considering the limbs as cooperative agents that form a hierarchy of cooperative teams: the whole body. The movements are analyzed at individual level and at team level using a modular hierarchical structure. Knowledge of the high-level team actions (such as ldquowalkingrdquo) improves the pertinence of our predictions on the low-level individual actions (foot is moving back and forth) and allows us to compensate for missing or noisy data produced by the feature extraction system. In terms of group behavior recognition, we propose a novel framework for online probabilistic plan recognition in cooperative multiagent systems: the Multiagent Hidden Markov mEmory Model (M-AHMEM), which is a dynamic Bayesian network. Experiments on an existing video database using different models of the human body show the feasibility of the approach.

[1]  Rama Chellappa,et al.  Shape and motion driven particle filtering for human body tracking , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[2]  Hung H. Bui,et al.  Efficient Approximate Inference for Online Probabilistic Plan Recognition , 2002 .

[3]  Suchi Saria,et al.  Probabilistic Plan Recognition in Multiagent Systems , 2004, ICAPS.

[4]  David C. Minnen,et al.  Propagation networks for recognition of partially ordered sequential action , 2004, CVPR 2004.

[5]  Michael P. Wellman,et al.  Probabilistic State-Dependent Grammars for Plan Recognition , 2000, UAI.

[6]  Benoît Macq,et al.  SILHOUETTE-BASED 2D MOTION CAPTURE FOR REAL-TIME APPLICATIONS , 2005 .

[7]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[8]  Nando de Freitas,et al.  Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.

[9]  Henry A. Kautz,et al.  Inferring High-Level Behavior from Low-Level Sensors , 2003, UbiComp.

[10]  Hector J. Levesque,et al.  Intention is Choice with Commitment , 1990, Artif. Intell..

[11]  Craig Boutilier,et al.  Context-Specific Independence in Bayesian Networks , 1996, UAI.

[12]  Svetha Venkatesh,et al.  Human action segmentation via controlled use of missing data in HMMs , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[13]  Henry A. Kautz,et al.  Generalized Plan Recognition , 1986, AAAI.

[14]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[15]  Robert P. Goldman,et al.  A Bayesian Model of Plan Recognition , 1993, Artif. Intell..

[16]  Ronen Basri,et al.  Actions as Space-Time Shapes , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Svetha Venkatesh,et al.  Recognizing and monitoring high-level behaviors in complex spatial environments , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[18]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[19]  Eric Horvitz,et al.  Layered representations for human activity recognition , 2002, Proceedings. Fourth IEEE International Conference on Multimodal Interfaces.