A User Pattern Learning Strategy for Managing Users' Mobility in UMTS Networks
IEEE Transactions on Mobile Computing
A Multi-stage Competitive Neural Networks Approach for Motion Trajectory Pattern Learning
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Detecting abnormal human behaviour using multiple cameras
Signal Processing
Joint trajectory tracking and recognition based on bi-directional nonlinear learning
Image and Vision Computing
Recognizing Human Actions Using Silhouette-based HMM
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Modeling of dynamics using process state projection on the self organizing map
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A multi-agent architecture for supporting distributed normality-based intelligent surveillance
Engineering Applications of Artificial Intelligence
Surveillance and human-computer interaction applications of self-growing models
Applied Soft Computing
Developing intelligent surveillance systems with an agent platform
KES-AMSTA'12 Proceedings of the 6th KES international conference on Agent and Multi-Agent Systems: technologies and applications
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The understanding and description of object behaviors is a hot topic in computer vision. Trajectory analysis is one of the basic problems in behavior understanding, and the learning of trajectory patterns that can be used to detect anomalies and predict object trajectories is an interesting and important problem in trajectory analysis. In this paper, we present a hierarchical self-organizing neural network model and its application to the learning of trajectory distribution patterns for event recognition. The distribution patterns of trajectories are learnt using a hierarchical self-organizing neural network. Using the learned patterns, we consider anomaly detection as well as object behavior prediction. Compared with the existing neural network structures that are used to learn patterns of trajectories, our network structure has smaller scale and faster learning speed, and is thus more effective. Experimental results using two different sets of data demonstrate the accuracy and speed of our hierarchical self-organizing neural network in learning the distribution patterns of object trajectories.