Pedestrian attribute classification with multi-scale and multi-label convolutional neural networks

作     者：朱建清 Zeng Huanqiang Zhang Yuzhao Zheng Lixin Cai Canhui 

作者机构：Fujian Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems College of Engineering Huaqiao University Quanzhou 362021 P. R. China School of Information Science and Engineering Huaqiao University Xiamen 361021 P. R. China 

出 版 物：《High Technology Letters》 (高技术通讯（英文版）)

年 卷 期：2018年第24卷第1期

页      面：53-61页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the National Natural Science Foundation of China(No.61602191,61672521,61375037,61473291,61572501,61572536,61502491,61372107,61401167) the Natural Science Foundation of Fujian Province(No.2016J01308) the Scientific and Technology Funds of Quanzhou(No.2015Z114) the Scientific and Technology Funds of Xiamen(No.3502Z20173045) the Promotion Program for Young and Middle aged Teacher in Science and Technology Research of Huaqiao University(No.ZQN-PY418,ZQN-YX403) the Scientific Research Funds of Huaqiao University(No.16BS108) 

主　　题：pedestrian attribute classification multi-scale features multi-label classification convolutional neural network (CNN) 

摘      要：Pedestrian attribute classification from a pedestrian image captured in surveillance scenarios is challenging due to diverse clothing appearances,varied poses and different camera views. A multiscale and multi-label convolutional neural network( MSMLCNN) is proposed to predict multiple pedestrian attributes simultaneously. The pedestrian attribute classification problem is firstly transformed into a multi-label problem including multiple binary attributes needed to be classified. Then,the multi-label problem is solved by fully connecting all binary attributes to multi-scale features with logistic regression functions. Moreover,the multi-scale features are obtained by concatenating those featured maps produced from multiple pooling layers of the MSMLCNN at different scales. Extensive experiment results show that the proposed MSMLCNN outperforms state-of-the-art pedestrian attribute classification methods with a large margin.