Visual motion pattern extraction and fusion for collision detection in complex dynamic scenes

Yue, Shigang and Rind, F. Claire (2006) Visual motion pattern extraction and fusion for collision detection in complex dynamic scenes. Computer vision and image understanding, 104 (1). pp. 48-60. ISSN 1090-235X

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1016/j.cviu.2006.07.002

Abstract

Detecting colliding objects in complex dynamic scenes is a difficult task for conventional computer vision techniques. However, visual processing mechanisms in animals such as insects may provide very simple and effective solutions for detecting colliding objects in complex dynamic scenes. In this paper, we propose a robust collision detecting system, which consists of a lobula giant movement detector (LGMD) based neural network and a translating sensitive neural network (TSNN), to recognise objects on a direct collision course in complex dynamic scenes. The LGMD based neural network is specialized for recognizing looming objects that are on a direct collision course. The TSNN, which fuses the extracted visual motion cues from several whole field direction selective neural networks, is only sensitive to translating movements in the dynamic scenes. The looming cue and translating cue revealed by the two specialized visual motion detectors are fused in the present system via a decision making mechanism. In the system, the LGMD plays a key role in detecting imminent collision; the decision from TSNN becomes useful only when a collision alarm has been issued by the LGMD network. Using driving scenarios as an example, we showed that the bio-inspired system can reliably detect imminent colliding objects in complex driving scenes.

Item Type:Article
Additional Information:Detecting colliding objects in complex dynamic scenes is a difficult task for conventional computer vision techniques. However, visual processing mechanisms in animals such as insects may provide very simple and effective solutions for detecting colliding objects in complex dynamic scenes. In this paper, we propose a robust collision detecting system, which consists of a lobula giant movement detector (LGMD) based neural network and a translating sensitive neural network (TSNN), to recognise objects on a direct collision course in complex dynamic scenes. The LGMD based neural network is specialized for recognizing looming objects that are on a direct collision course. The TSNN, which fuses the extracted visual motion cues from several whole field direction selective neural networks, is only sensitive to translating movements in the dynamic scenes. The looming cue and translating cue revealed by the two specialized visual motion detectors are fused in the present system via a decision making mechanism. In the system, the LGMD plays a key role in detecting imminent collision; the decision from TSNN becomes useful only when a collision alarm has been issued by the LGMD network. Using driving scenarios as an example, we showed that the bio-inspired system can reliably detect imminent colliding objects in complex driving scenes.
Keywords:Visual motion, Pattern recognition, Collision detection, Neural network, Locust, Lobula giant movement detector (LGMD), Direction selectivity, Complex dynamic scene, Nature-inspired information processing
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
ID Code:1220
Deposited By: Jill Partridge
Deposited On:24 Sep 2007
Last Modified:18 Jul 2011 16:17

Repository Staff Only: item control page