Pedestrian Detection Algorithm Based on Deep Feature Attention Extraction
Pedestrian detection is one of the basic challenges of computer vision.The purpose is to locate pedestrians quickly and accurately.Aiming at the problems of locating large targets and detecting small targets in pedestrian detection and crowded crowds,a pedestrian detection algorithm based on deep feature attention extraction is proposed by enlarging the receptive field of deep feature map and enriching the deep feature information.Firstly,a new stage is added to the backbone network ResNet-50,and deeper feature extraction is carried out through empty convolution to capture rich context features.Secondly,the attention mecha-nism is used to mine the correlation information between features from the perspective of channel dimension to enhance pedestrian features.Although the proposed pedestrian detection algorithm has a simple structure,it shows competitive accuracy and good speed on the challenging pedestrian detection benchmark CityPersons and Caltech datasets.
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