Abstract
In order to improve the accuracy, real-time and robustness of pedestrian video detection, this paper presents an improved fast multiscale pedestrian detection algorithm based on integral channel features. The captured video is pretreated which is transformed RGB image into LUV image, and then the image pyramid is calculated to obtain the channel features at different scales. The improved fast image pyramid algorithm is used to estimate the other layer features of the pyramid. Traversing all the pyramid image by the sliding window to extract features. Finally, the features are input into the trained cascade AdaBoost classifier to find the most representative pedestrian characteristics. The experimental results show that the proposed algorithm in this paper can detect pedestrians accurately and meet the requirements of good accuracy, real-time and robustness with different posture and the variety of complex backgrounds.
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