Competition-Based Induction of Decision Models from Examples
Machine Learning - Special issue on genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A fuzzy temporal rule-based velocity controller for mobile robotics
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
Technologies for constructing intelligent systems
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Robust Tracking of Multiple People in Crowds Using Laser Range Scanners
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Fusion of color and infrared video for moving human detection
Pattern Recognition
A real-time object detecting and tracking system for outdoor night surveillance
Pattern Recognition
Search-intensive concept induction
Evolutionary Computation
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
Fuzzy temporal rules for mobile robot guidance in dynamicenvironments
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Fuzzy Systems
Omnivision-based KLD-Monte Carlo Localization
Robotics and Autonomous Systems
Semantic fusion of laser and vision in pedestrian detection
Pattern Recognition
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The knowledge about the position and movement of people is of great importance in mobile robotics for implementing tasks such as navigation, mapping, localization, or human-robot interaction. This knowledge enhances the robustness, reliability and performance of the robot control architecture. In this paper, a pattern classifier system for the detection of people using laser range finders data is presented. The approach is based on the quantified fuzzy temporal rules (QFTRs) knowledge representation and reasoning paradigm, that is able to analyze the spatio-temporal patterns that are associated to people. The pattern classifier system is a knowledge base made up of QFTRs that were learned with an evolutionary algorithm based on the cooperative-competitive approach together with token competition. A deep experimental study with a Pioneer II robot involving a five-fold cross-validation and several runs of the genetic algorithm has been done, showing a classification rate over 80%. Moreover, the characteristics of the tests represent complex and realistic conditions (people moving in groups, the robot moving in part of the experiments, and the existence of static and moving people).