A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient region-based motion segmentation for a video monitoring system
Pattern Recognition Letters
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design and application of hybrid intelligent systems
Illumination Normalization with Time-Dependent Intrinsic Images for Video Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A revaluation of frame difference in fast and robust motion detection
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
Performance evaluation of a real time video surveillance system
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
NSP: a Neuro---Symbolic Processor
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Heuristic algorithms for fast and accurate tracking of moving objects in unrestricted environments
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
Hi-index | 0.00 |
In this paper we propose a new approach to active video surveillance intelligence systems based on the integration of artificial neural networks (ANN) and symbolic Artificial Intelligence (AI). In particular, the neurosymbolic hybrid system here presented is formed by virtual neural sensors (WiSARD--like systems) and BDI agents. The coupling of virtual neural sensors with symbolic reasoning for interpreting their outputs, makes this approach both very light from the computational and hardware point of view and quite robust in performances.