A synthetic vision system using directionally selective motion detectors to recognize collision

Yue, Shigang and Rind, F. Claire (2007) A synthetic vision system using directionally selective motion detectors to recognize collision. Artificial life, 13 (2). pp. 93-122. ISSN 1530-9185

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Full text URL: http://dx.doi.org/10.1162/artl.2007.13.2.93

Abstract

Reliably recognizing objects approaching on a collision course is extremely important. A synthetic vision system is proposed to tackle the problem of collision recognition in dynamic environments. The system combines the outputs of four whole-field motion-detecting neurons, each receiving inputs from a network of neurons employing asymmetric lateral inhibition to suppress their responses to one direction of motion. An evolutionary algorithm is then used to adjust the weights between the four motion-detecting neurons to tune the system to detect collisions in two test environments. To do this, a population of agents, each representing a proposed synthetic visual system, either were shown images generated by a mobile Khepera robot navigating in a simplified laboratory environment or were shown images videoed outdoors from a moving vehicle. The agents had to cope with the local environment correctly in order to survive. After 400 generations, the best agent recognized imminent collisions reliably in the familiar environment where it had evolved. However, when the environment was swapped, only the agent evolved to cope in the robotic environment still signaled collision reliably. This study suggests that whole-field direction-selective neurons, with selectivity based on asymmetric lateral inhibition, can be organized into a synthetic vision system, which can then be adapted to play an important role in collision detection in complex dynamic scenes.

Item Type:Article
Additional Information:Reliably recognizing objects approaching on a collision course is extremely important. A synthetic vision system is proposed to tackle the problem of collision recognition in dynamic environments. The system combines the outputs of four whole-field motion-detecting neurons, each receiving inputs from a network of neurons employing asymmetric lateral inhibition to suppress their responses to one direction of motion. An evolutionary algorithm is then used to adjust the weights between the four motion-detecting neurons to tune the system to detect collisions in two test environments. To do this, a population of agents, each representing a proposed synthetic visual system, either were shown images generated by a mobile Khepera robot navigating in a simplified laboratory environment or were shown images videoed outdoors from a moving vehicle. The agents had to cope with the local environment correctly in order to survive. After 400 generations, the best agent recognized imminent collisions reliably in the familiar environment where it had evolved. However, when the environment was swapped, only the agent evolved to cope in the robotic environment still signaled collision reliably. This study suggests that whole-field direction-selective neurons, with selectivity based on asymmetric lateral inhibition, can be organized into a synthetic vision system, which can then be adapted to play an important role in collision detection in complex dynamic scenes.
Keywords:Complex dynamic scene, Direction selectivity, Asymmetric lateral inhibition, Collision detection, Evolution, Synthetic vision
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
ID Code:1218
Deposited By: Jill Partridge
Deposited On:24 Sep 2007
Last Modified:18 Jul 2011 16:17

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