Probabilistic Recognition of Complex Event

This paper describes a complex event recognition approach with probabilistic reasoning for handling uncertainty. The first advantage of the proposed approach is the flexibility of the modeling of composite events with complex temporal constraints. The second advantage is the use of probability theory providing a consistent framework for dealing with uncertain knowledge for the recognition of complex events. The experimental results show that our system can successfully improve the event recognition rate. We conclude by comparing our algorithm with the state of the art and showing how the definition of event models and the probabilistic reasoning can influence the results of the real-time event recognition.

[1]  Larry S. Davis,et al.  VidMAP: video monitoring of activity with Prolog , 2005, IEEE Conference on Advanced Video and Signal Based Surveillance, 2005..

[2]  Eric Horvitz,et al.  A Comparison of HMMs and Dynamic Bayesian Networks for Recognizing Office Activities , 2005, User Modeling.

[3]  Duc Phu Chau,et al.  Robust Mobile Object Tracking based on Multiple Feature Similarity and Trajectory Filtering , 2011, VISAPP.

[4]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[5]  Aaron F. Bobick,et al.  Recognition of Visual Activities and Interactions by Stochastic Parsing , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Paul Brna,et al.  User Modeling 2005, 10th International Conference, UM 2005, Edinburgh, Scotland, UK, July 24-29, 2005, Proceedings , 2005, User Modeling.

[7]  Ramakant Nevatia,et al.  Video-based event recognition: activity representation and probabilistic recognition methods , 2004, Comput. Vis. Image Underst..

[8]  Henry A. Kautz,et al.  Sensor-Based Understanding of Daily Life via Large-Scale Use of Common Sense , 2006, AAAI.

[9]  Chris D. Nugent,et al.  Ontology-based activity recognition in intelligent pervasive environments , 2009, Int. J. Web Inf. Syst..

[10]  Shaogang Gong,et al.  Recognition of group activities using dynamic probabilistic networks , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[11]  Svetha Venkatesh,et al.  Activity recognition and abnormality detection with the switching hidden semi-Markov model , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  Henry A. Kautz,et al.  Location-Based Activity Recognition using Relational Markov Networks , 2005, IJCAI.

[13]  Ben Taskar,et al.  Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) , 2007 .

[14]  Jake K. Aggarwal,et al.  Semantic Representation and Recognition of Continued and Recursive Human Activities , 2009, International Journal of Computer Vision.

[15]  Stuart M. Shieber,et al.  Recognition of Users' Activities Using Constraint Satisfaction , 2009, UMAP.

[16]  François Brémond,et al.  Automatic Video Interpretation: A Recognition Algorithm for Temporal Scenarios Based on Pre-compiled Scenario Models , 2003, ICVS.

[17]  Luc Van Gool,et al.  What's going on? Discovering spatio-temporal dependencies in dynamic scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Jesse Hoey,et al.  Assisting persons with dementia during handwashing using a partially observable Markov decision process. , 2007, ICVS 2007.

[19]  Francesco Ricci,et al.  User Modeling, Adaptation, and Personalization , 2013, Lecture Notes in Computer Science.

[20]  François Brémond,et al.  Design and Assessment of an Intelligent Activity Monitoring Platform , 2005, EURASIP J. Adv. Signal Process..

[21]  Andrew McCallum,et al.  Introduction to Statistical Relational Learning , 2007 .

[22]  François Brémond,et al.  Automatic Video Interpretation: A Novel Algorithm for Temporal Scenario Recognition , 2003, IJCAI.