Efficient multi-scale pedestrian detection algorithm withfeature map aggregation

An efficient multi-scale pedestrian detection algorithm with convolutional neural network feature map aggregation was proposed for the problems of low accuracy and efficiency in pedestrian detection algorithm trained by manual design feature. An aggregation network was designed to gather high-level and low-level feature maps to construct a feature map with the ability of spatial resolution and semantic. And an extension network was constructed to provide feature maps for multi-scale detection. In addition, candidate areas were redesigned to construct a multi-scale detection network to improve positioning accuracy. The feature map aggregation network, extension network and multi-scale pedestrian detection network were combined for an end-to-end training. The experimental results show that, compared to algorithms based on manual design features, the proposed algorithm can effectively improve the accuracy of pedestrian detection and positioning. Under common hardware conditions, the proposed approach can provide real-time detection. © 2019, Zhejiang University Press. All right reserved.