A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biologically Inspired Saliency Map Model for Bottom-up Visual Attention
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
A Goal Oriented Attention Guidance Model
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
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The conventional side-view and rear-view mirrors are not enough for driver's safety in an automobile. A driver may not be able to recognize the vehicle in a blind spot. In this paper, we propose an automotive detector algorithm using biologically motivated selective attention model for a blind spot monitor. This method decides a region of interest (ROI) which includes the blind spot from the successive image frames obtained by side-view cameras. It can detect the dangerous situations in the ROI using novelty points from the biologically motivated selective attention model, and alerts the driver whether there is dangerous object for changing the lane in driving. The proposed algorithm is based on deciding the ROI using difference from intensity histogram of a Gaussian smoothed image and finding the novelty points from the biologically motivated selective attention model. From variations of those novelty points, we determine whether a vehicle is approaching or not.