Self-organizing neural networks for perception of visual motion
Neural Networks
Vision for Mobile Robot Navigation: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Silicon Implementation of the Fly's Optomotor Control System
Neural Computation
Expansion segmentation for visual collision detection and estimation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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
Hi-index | 0.00 |
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.