Abstract: In order to reduce the energy consumption and safety warning cost of the driver’s observation and judgment of the left front,right front blind area and the surrounding during driving,this paper studies algorithms and technologies related to pedes-trian safety automatic detection and ranging in the driver’s blind area,and proposes a driver’s blind area safety warning scheme based on machine vision.Firstly,based on the driver’s actual driving perspective,through the study on the image pedestrian recognition features,the multifeature fusion blind area pedestrian satety detection method is designed,the feature histogram of the fusion scheme is obtained for the positive and negative classification of automatic pedestrian detection,and GPU is introduced to accelerate the processing efficiency of data sharing transactions.Secondly,based on the interpolation measurement method and monocular ranging principle,the laboratory ranging is carried out,some pixels are calibrated and different location points are calibrated in combination with the actual scene,so as to improve the measurement accuracy and calculation speed under the fixed angle scene,and optimize the monocular camera ranging method under the vehicle video scene.the pedestrians on the left and right sides of the car are automatically identified and ranging,and the optimal reminder distance is calculated according to the driver’s reaction time and the braking distance of the car,and the driver is reminded appropriately to reduce the probability of accidents.Experimental results show that the scheme can effectively identify pedestrians and measure the distance,ensure the real-time reminder to drivers,and has low economic cost and good practicability.

Key words: Blind area warning, Machine vision, Pedestrian detection, Feature fusion, Ranging