Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Video-Based Surveillance Systems: Computer Vision and Distributed Processing
Video-Based Surveillance Systems: Computer Vision and Distributed Processing
DETER: detection of events for threat evaluation and recognition
Machine Vision and Applications
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Anomaly Detection for Video Surveillance Applications
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Kernel estimation of density level sets
Journal of Multivariate Analysis
Intelligent Distributed Video Surveillance Systems (Professional Applications of Computing) (Professional Applications of Computing)
Artificial Intelligence and Integrated Intelligent Information Systems: Emerging Technologies and Applications
A network of sensor-based framework for automated visual surveillance
Journal of Network and Computer Applications
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
IEEE Transactions on Pattern Analysis and Machine Intelligence
AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
Multi-agent Based Distributed Video Surveillance System over IP
ISCSCT '08 Proceedings of the 2008 International Symposium on Computer Science and Computational Technology - Volume 02
A survey on behavior analysis in video surveillance for homeland security applications
AIPR '08 Proceedings of the 2008 37th IEEE Applied Imagery Pattern Recognition Workshop
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition using PCA and SVM
ASID'09 Proceedings of the 3rd international conference on Anti-Counterfeiting, security, and identification in communication
Privacy-preserving collaborative anomaly detection
Privacy-preserving collaborative anomaly detection
A PCA-based technique to detect moving objects
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Online anomaly detection using KDE
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Real-time detection of unusual regions in image streams
Proceedings of the international conference on Multimedia
Unusual activity detection for video surveillance
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
The Normalized Compression Distance Is Resistant to Noise
IEEE Transactions on Information Theory
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Many schemes have been presented over the years to develop automated visual surveillance systems. However, these schemes typically need custom equipment, or involve significant complexity and storage requirements. In this paper we present three software-based agents built using kernel machines to perform automated, real-time intruder detection in surveillance systems. Kernel machines provide a powerful data mining technique that may be used for pattern matching in the presence of complex data. They work by first mapping the raw input data onto a (often much) higher-dimensional feature space, and then clustering in the feature space instead. The reasoning is that mapping onto the (higher-dimensional) feature space enables the comparison of additional, higher-order correlations in determining patterns between the raw data points. The agents proposed here have been built using algorithms that are adaptive, portable, do not require any expensive or sophisticated components, and are lightweight and efficient having run times of the order of hundredths of a second. Through application to real image streams from a simple, run-of-the-mill closed-circuit television surveillance system, and direct quantitative performance comparison with some existing schemes, we show that it is possible to easily obtain high detection accuracy with low computational and storage complexities.