Autonomous Detector Using Saliency Map Model and Modified Mean-Shift Tracking for a Blind Spot Monitor in a Car

We propose an autonomous blind spot monitoring method using a morphology-based saliency map (SM) model and the method of combining scale invariant feature transform (SIFT) with mean-shift tracking algorithm. The proposed method decides a region of interest (ROI) which includes the blind spot from the successive image frames obtained by side-view cameras. Topology information of the salient areas obtained from the SM model is used to detect a candidate of dangerous situations in the ROI, and the SIFT algorithm is considered for verifying whether the localized candidate area contains an automobile. We developed a modified mean-shift algorithm to track the detected automobile in a blind spot area. The modified mean-shift algorithm uses the orientation probability histogram for tracking the automobile around the localized area. Experimental results show that the proposed algorithm successfully provides an alarm signal to the driver in a dangerous situations caused by approaching an automobile at side-view.

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