Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
RoboCup: A Challenge Problem for AI and Robotics
RoboCup-97: Robot Soccer World Cup I
Fast Image Segmentation, Object Recognition and Localization in a RoboCup Scenario
RoboCup-99: Robot Soccer World Cup III
Techniques for Obtaining Robust, Real-Time, Colour-Based Vision for Robotics
RoboCup-99: Robot Soccer World Cup III
A Segmentation System for Soccer Robot Based on Neural Networks
RoboCup-99: Robot Soccer World Cup III
A domain-independentwindow approach to multiclass object detection using genetic programming
EURASIP Journal on Applied Signal Processing
Bloat control in genetic programming by evaluating contribution of nodes
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Motion detection in complex environments by genetic programming
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Learning Motion Detectors by Genetic Programming
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Hi-index | 0.00 |
In this paper we investigated the use of Genetic Programming (GP) to evolve programs which could detect moving objects in videos. Two main approaches under the paradigm were proposed and investigated, single-frame approach and multi-frame approach. The former is based on analyzing individual video frames and treat them independently while the latter approach consider a sequence of frames. In the single-frame approach, three methods are investigated including using pixel intensity, pixel hue value and feature values. The experiments on Robosoccer field show that GP could detect the target under different lighting conditions and could even handle arbitrary camera positions. Although there was no domain knowledge had been provided during evolution, GP was able to produce moving object detectors that were robust and fast.