Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Visual motion pattern extraction and fusion for collision detection in complex dynamic scenes
Computer Vision and Image Understanding
A Robot Vision System for Collision Avoidance Using a Bio-inspired Algorithm
Neural Information Processing
A mixed analog-digital vision sensor for detecting objects approaching on a collision course
Robotics and Autonomous Systems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Constraints of biological neural networks and their consideration in AI applications
Advances in Artificial Intelligence - Special issue on artificial intelligence in neuroscience and systems biology: lessons learnt, open problems, and the road ahead
A modified model for the Lobula Giant Movement Detector and its FPGA implementation
Computer Vision and Image Understanding
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The lobula giant movement detector (LGMD) neuron of locusts has been shown to preferentially respond to objects approaching the eye of a locust on a direct collision course. Computer simulations of the neuron have been developed and have demonstrated the ability of mobile robots, interfaced with a simulated LGMD model, to avoid collisions. In this study, a model of the LGMD neuron is presented and the functional parameters of the model identified. Models with different parameters were presented with a range of automotive video sequences, including collisions with cars. The parameters were optimised to respond correctly to the video sequences using a range of genetic algorithms (GAs). The model evolved most rapidly using GAs with high clone rates into a form suitable for detecting collisions with cars and not producing false collision alerts to most non-collision scenes.