Pedestrian Attribute Recognition At Far Distance

被引：273

作者：

Deng, Yubin ^{[1
]}

Luo, Ping ^{[1
]}

Loy, Chen Change ^{[1
]}

Tang, Xiaoou ^{[1
]}

机构：

[1] Chinese Univ Hong Kong, Dept Informat Engn, Hong Kong, Peoples R China

来源：

PROCEEDINGS OF THE 2014 ACM CONFERENCE ON MULTIMEDIA (MM'14) | 2014年

关键词：

Large-scale database; attribute classification;

D O I：

10.1145/2647868.2654966

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The capability of recognizing pedestrian attributes, such as gender and clothing style, at far distance, is of practical interest in far-view surveillance scenarios where face and body close-shots are hardly available. We make two contributions in this paper. First, we release a new pedestrian attribute dataset, which is by far the largest and most diverse of its kind. We show that the large-scale dataset facilitates the learning of robust attribute detectors with good generalization performance. Second, we present the benchmark performance by SVM-based method and propose an alternative approach that exploits context of neighboring pedestrian images for improved attribute inference.

引用

页码：789 / 792

页数：4