Vehicle Segmentation and Classification Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Measure Theory
Pattern Recognition by Self-Organizing Neural Networks
Pattern Recognition by Self-Organizing Neural Networks
Robust Multiple Car Tracking with Occlusion Reasoning
Robust Multiple Car Tracking with Occlusion Reasoning
A road sign recognition system based on dynamic visual model
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Discrimination of the road condition toward understanding of vehicle driving environments
IEEE Transactions on Intelligent Transportation Systems
An obstacle detection method by fusion of radar and motion stereo
IEEE Transactions on Intelligent Transportation Systems
Depth-based target segmentation for intelligent vehicles: fusion of radar and binocular stereo
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
On-road vehicle detection using evolutionary Gabor filter optimization
IEEE Transactions on Intelligent Transportation Systems
Reliable method for driving events recognition
IEEE Transactions on Intelligent Transportation Systems
Reliable Detection of Overtaking Vehicles Using Robust Information Fusion
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Toward Autonomous Collision Avoidance by Steering
IEEE Transactions on Intelligent Transportation Systems
Sensor Fusion for Predicting Vehicles' Path for Collision Avoidance Systems
IEEE Transactions on Intelligent Transportation Systems
GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection
IEEE Transactions on Image Processing
A channel awareness vehicle detector
IEEE Transactions on Intelligent Transportation Systems
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Driving always involves risk. Various means have been proposed to reduce the risk. Critical motion detection of nearby moving vehicles is one of the important means of preventing accidents. In this paper, a computational model, which is referred to as the dynamic visual model (DVM), is proposed to detect critical motions of nearby vehicles while driving on a highway. The DVM is motivated by the human visual system and consists of three analyzers: 1) sensory analyzers, 2) perceptual analyzers, and 3) conceptual analyzers. In addition, a memory, which is called the episodic memory, is incorporated, through which a number of features of the system, including hierarchical processing, configurability, adaptive response, and selective attention, are realized. A series of experimental results with both single and multiple critical motions are demonstrated and show the feasibility of the proposed system.