W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning variable-length Markov models of behavior
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Real-Time Multiple Objects Tracking with Occlusion Handling in Dynamic Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
Detecting and discriminating behavioural anomalies
Pattern Recognition
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In this paper, we propose a new approach to multi-people activity recognition in outdoor scenes. The proposed method is based on Hidden Markov Models with parameters of reduced dimensionality. Most existing work is based on HMMs and DBNs, and focuses on the interactions between two objects. However, longer feature vectors of HMMs usually lead to covariance matrix singularity in parameter learning and activity recognition. Moreover, arbitrary structure of DBNs can introduce large computational complexity. Compared with former works, the proposed method named PCA-HMMs reduces the dimensionality of the model parameters while retains most of the original variability, and thus avoids overflowing and weakens the constraints on observations in conventional HMMs. The experimental results proved that the modified HMMs are effective solutions for multi-people interactive activity recognition.