Learning invariance from transformation sequences
Neural Computation
Learning the distribution of object trajectories for event recognition
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimedia Video-Based Surveillance Systems: Requirements, Issues and Solutions
Multimedia Video-Based Surveillance Systems: Requirements, Issues and Solutions
A hybrid learning network for shift-invariant recognition
Neural Networks
Application of the Self-Organizing Map to Trajectory Classification
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
IEEE Transactions on Neural Networks
Multiple object tracking using a neural cost function
Image and Vision Computing
FPGA-Based Anomalous Trajectory Detection Using SOFM
ARC '09 Proceedings of the 5th International Workshop on Reconfigurable Computing: Architectures, Tools and Applications
Behavioral analysis of mobile robot trajectories using a point distribution model
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
L1 norm based KPCA for novelty detection
Pattern Recognition
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A hierarchical self-organising neural network is described for the detection of unusual pedestrian behaviour in video-based surveillance systems. The system is trained on a normal data set, with no prior information about the scene under surveillance, thereby requiring minimal user input. Nodes use a trace activation rule and feedforward connections that are modified so higher layer nodes are sensitive to trajectory segments traced across the previous layer. Top layer nodes have binary lateral connections and corresponding "novelty accumulator" nodes. Lateral connections are set between co-occurring nodes, generating a signal to prevent accumulation of the novelty measure along normal sequences. In abnormal sequences the novelty accumulator nodes are allowed to increase their activity, generating an alarm state.