Recognizing Interaction Activities using Dynamic Bayesian Network

Authors:
Youtian Du;Feng Chen;Wenli Xu;Yongbin Li
Affiliations:
Tsinghua University, Beijing, 100084, China.;Tsinghua University, Beijing, 100084, China.;Tsinghua University, Beijing, 100084, China.;Tsinghua University, Beijing, 100084, China.
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Year:
2006

Citing 0
Cited 15

Activity recognition through multi-scale motion detail analysis

Neurocomputing
Non Parametric Classification of Human Interaction

IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Semantic retrieval of events from indoor surveillance video databases

Pattern Recognition Letters
Review: The use of pervasive sensing for behaviour profiling - a survey

Pervasive and Mobile Computing
Sensor-Based Human Activity Recognition in a Multi-user Scenario

AmI '09 Proceedings of the European Conference on Ambient Intelligence
Real-time activity classification using ambient and wearable sensors

IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Recognition of user activity sequences using distributed event detection

EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
Decomposition in hidden Markov models for activity recognition

MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Hand gesture recognition based on dynamic Bayesian network framework

Pattern Recognition
Detecting and discriminating behavioural anomalies

Pattern Recognition
Hierarchical visual event pattern mining and its applications

Data Mining and Knowledge Discovery
Recognizing multi-user activities using wearable sensors in a smart home

Pervasive and Mobile Computing
Incremental behavior modeling and suspicious activity detection

Pattern Recognition
Shopping behavior recognition using a language modeling analogy

Pattern Recognition Letters
One-shot learning gesture recognition from RGB-D data using bag of features

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Activity recognition is significant in intelligent surveillance. In this paper, we present a novel approach to the recognition of interacting activities based on dynamic Bayesian network (DBN). In this approach the features representing the object motion are divided into two classes: global features and local features, which are at two different spatial scales. Global features describe object motion at a large spatial scale and relations between objects or between the object and environment, and local ones represent the motion details of objects of interest. We propose a new DBN model structure with state duration to model human interacting activities. This DBN model structure combines the global features with local ones harmoniously. The effectiveness of this novel approach is demonstrated by experiment.