This paper presents a rear obstacle detection system by using a single rear view camera. The system can detect various static and moving obstacles behind the cars. An efficient hierarchical detecting strategy is used to achieve high detection rate and low false positives. The temporal Inverse perspective mapping difference image based coarse detection is used to estimate whether there are obstacles in the predetermined warning area at first stage. Then a novel integral image based segmentation algorithm is developed for fine obstacle segmentation. Finally, the blob analysis is utilized for obstacle representation and verification. Our system achieves 94.2% detection rate and 16% false positives rate on 125 challenging video sequences. The average processing speed of the system is 25fps on a standard laptop.
In this paper, an efficient method for single image dehazing is proposed based on haze density estimation. We provide two forms of haze density estimation in different color spaces, which are called scene-based haze density estimation in HSV color space and pixel-based haze density estimation in RGB color space. The attenuation model of pixel-level transmission is established based on the two haze density estimations by an exponential function. Guided filtering is applied to smooth the transmission map and maintain the local edges. Global atmospheric light is obtained adaptively by smoothed transmission. A series of experiments on different types of hazy images are implemented, and the results reveal that the proposed method can obtain high-quality haze-free images along with abundant details, high color fidelity, and few halo artifacts.
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