Digital Image Processing
Depth-based target segmentation for intelligent vehicles: fusion of radar and binocular stereo
IEEE Transactions on Intelligent Transportation Systems
Dealing with the Perspective Distortion to Detect Overtaking Cars for Driving Assistance
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
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Although motion extraction requires high computational resources and normally produces very noisy patterns in real sequences, it provides useful cues to achieve an efficient segmentation of independent moving objects. Our goal is to employ basic knowledge about biological vision systems to address this problem. We use the Reichardt motion detectors as first extraction primitive to characterize the motion in scene. The saliency map is noisy, therefore we use a neural structure that takes full advantage of the neural population coding, and extracts the structure of motion by means of local competition. This scheme is used to efficiently segment independent moving objects. In order to evaluate the model, we apply it to a real-life case of an automatic watch-up system for car-overtaking situations seen from the rear-view mirror. We describe how a simple, competitive, neural processing scheme can take full advantage of this motion structure for segmenting overtaking-cars.