Mono-camera based side vehicle detection for blind spot detection systems

This paper proposes a vision-based side vehicle detection for blind spot detection systems. The proposed algorithm uses a HoG cascade classifier in order to detect vehicles, and tracks the detected vehicles with Kalman filter. The proposed algorithm performs a periodical vehicle detection instead of every frame vehicle detection. And the proposed algorithm reduces the detecting image size by downscaling the original image and setting the region of interest where vehicles can exist. As a result, we can reduce the processing time for vehicle detection.

Also, the proposed algorithm uses a false alarm reducing methods by control the reliability points at vehicle tracking. We evaluated the performance of the proposed algorithm in terms of processing time and detection ratio. At target board, the proposed algorithm has 40 frames per second, which meets the real time requirements of the ADAS systems. The detection ratio of the proposed algorithm is over 96 % at both original image size and downscale image size.

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