GoogLeNet-like Model for Pedestrian Attribute Detection in Surveillance Environment
Changhong Jing, Song Cao, Yanyan Shen, Shuqiang Wang · 2021 7th International Conference on Computer and Communications (ICCC) · 2021
Social public security has attracted worldwide attention in these years. Automatic pedestrian attribute detection is of great significance to improve monitoring efficiency. Low resolution of surveillance images and multi-angle problem of image acquisition are the main challenges of current pattern recognition researches. In order to improve the pedestrian attribute detection accuracy, we proposed a GoogLeNet-like model with inception structure which replaced the optimal local construction by a series of dense substructures. The experimental results show that the proposed method outperforms the previous works.