A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Evaluation of global image thresholding for change detection
Pattern Recognition Letters
Combining Intelligent Techniques for Sensor Fusion
Applied Intelligence
Aircraft identification integrated into an airport surface surveillance video system
Machine Vision and Applications
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Evolving parameters of surveillance video systems for non-overfitted learning
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
| Hi-index | 0.00 |
The purpose of this research is to apply data mining (DM) to an optimized surveillance video system with the objective of improving tracking robustness and stability. Specifically, the machine learning has been applied to blob extraction and detection, in order to decide whether a detected blob corresponds to a real target or not. Performance is assessed with an Evaluation function, which has been developed for optimizing the video surveillance system. This Evaluation function measures the quality level reached by the tracking system.