Obstacle Avoidance Using Flow Field Divergence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Tracking Strategy for Monocular Depth Inference over Multiple Frames
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
Real-time obstacle avoidance using central flow divergence and peripheral flow
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Monocular Collision Warning System
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Time-to-Collision Estimation from Motion Based on Primate Visual Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual motion pattern extraction and fusion for collision detection in complex dynamic scenes
Computer Vision and Image Understanding
GOLD: A Framework for Developing Intelligent-Vehicle Vision Applications
IEEE Intelligent Systems
Stereo-based pedestrian detection for collision-avoidance applications
IEEE Transactions on Intelligent Transportation Systems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Estimating the driving state of oncoming vehicles from a moving platform using stereo vision
IEEE Transactions on Intelligent Transportation Systems
Realtime depth estimation and obstacle detection from monocular video
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Sensor Fusion for Predicting Vehicles' Path for Collision Avoidance Systems
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Neural Networks
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Bio-inspired vision sensors are particularly appropriate candidates for navigation of vehicles or mobile robots due to their computational simplicity, allowing compact hardware implementations with low power dissipation. The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of an approaching object and the proximity of this object. It has been found that it can respond to looming stimuli very quickly and trigger avoidance reactions. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper introduces a modified neural model for LGMD that provides additional depth direction information for the movement. The proposed model retains the simplicity of the previous model by adding only a few new cells. It has been simplified and implemented on a Field Programmable Gate Array (FPGA), taking advantage of the inherent parallelism exhibited by the LGMD, and tested on real-time video streams. Experimental results demonstrate the effectiveness as a fast motion detector.