Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (5): 29-34.doi: 10.16180/j.cnki.issn1007-7820.2021.05.006

Previous Articles     Next Articles

Pedestrian Detection Algorithm Based on Multiple Feature Fusion

GU Wei,LI Feifei,CHEN Qiu   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Online:2021-05-15 Published:2021-05-24
  • Supported by:
    The Program for Professor of Special Appointment(Eastern Scholar) at Shanghai Institutions of Higher Learning(ES2015XX)


As a typical problem of object detection, pedestrian detection has been an active research topic in recent years. Pedestrian detection is widely used in intelligent transportation, autonomous driving, video surveillance, behavior analysis and other fields, but there are many problems to be solved. In this study, a multi-channel feature model based on multiple feature fusion which consists of a non-deep learning branch, a body branch and a limb branch is proposed. A small number of high-quality pedestrian candidate areas are extracted through the non-deep learning branch, thus reducing the computational cost cause by exhaustive search by sliding window and improving the computational efficiency. The body branch and limb branch obtained from the feature of multi-layer convolutional channel are applied to detect pedestrians through the overall human body information and the semantic information of human body parts, respectively. Caltech pedestrian dataset and INRIA pedestrian dataset are adopted to train and test the proposed model. Experimental results show that combine with the output of each branch, the proposed pedestrian detector have a lower miss rate. The miss rates on INRIA pedestrian dataset and Caltech pedestrian dataset are 8.24% and 19.78%, respectively.

Key words: pedestrian detection, convolutional neural network, multi-channel feature, multi-layer convolution channel feature, locally decorrelated channel features, histogram of oriented gradient

CLC Number: 

  • TP391.41