关键词: 行人检测, 积分通道特征, 多尺度, 快速特征金字塔

Abstract: In order to overcome the problem that integral channel features have redundant information and slow detection speed in multiscale pedestrian detection, an improved integral channel features for fast multiscale pedestrian detection is proposed. Firstly, fast feature pyramids are used to compute multiscale channel features, which avoid computing the same features at multiple locations and scales. Then the detection windows are divided into cells and blocks to make the overall description of the image and reduce the redundancy, replacing the original method of random location and size. Finally, it obtains the sums of pixels in cells and blocks as the pedestrian features and then the features are classified by soft cascade Adaboost. Simulation experiment results show that the proposed method gives improved accuracy and 15.4 times speedup compared with the original algorithm and runs at 18.5 f/s on 640×480 images.

Key words: pedestrian detection, integral channel features, multi-scale, fast feature pyramids