Pedestrian Detection Based on F-DPM in Real Time
DAI Zhiyi;HUANG Miaohua;School of Automotive Engineering,Wuhan University of Technology;Hubei Province Key Laboratory of Modern Automotive Technology,Wuhan University of Technology;
Requiring the assistance of GPU restricts the practical application of deep learning method in the area of pedestrian detection. The real-time performance is poor,if low-level configuration of hardware is used. To cope with it,an improved algorithm based on the DPM,which is named F-DPM algorithm,is proposed in this paper. The F-DPM makes three improvements to DPM:(1) the feature pyramid is built quickly by reducing the dimension of soft binning histogram;(2) the convolution operation of feature and template is accelerated by FFT;(3) the accuracy of the target location is guaranteed by layer detection. Final results show that the precision of F-DPM algorithm reaches 85. 7%,the recall of the algorithm is 82. 9%,and the average speed of detection is 52 ms. Basically,it meets the real-time and accuracy requirements. Comparing with the ACF and YOLO algorithms,the test results of F-DPM is better in complex scene. Therefore,this F-DPM algorithm contributes to the practical application in detecting pedestrians.
【CateGory Index】： TP181;TP391.41