关键词: 行人检测, 卷积神经网路, 特征融合

Abstract: Pedestrian detection is an important research direction of object detection. A problem that the pedestrian detection algorithm misses detection in the case of complex scenes and objects that are too small. Based on the Faster R-CNN detection algorithm, a deep network pedestrian detection based on shallow feature fusion guidance is proposed. Through the HOG feature, the improved LBP features and deep network features are combined to obtain accurate pedestrian characteristics, and a series of experiments are carried out on the internationally widely used pedestrian dataset. The results show that the proposed improved method has better performance in detecting accuracy and rate.

Key words: pedestrian detection, convolutional neural network, feature fusion