行人属性识别(Pedestrian Attribute Recognition)学习笔记

Pedestrian Attribute Recognition

Pedestrian attribution recognition is the task of recognising pedestrian features - such as whether they are talking on a phone, whether they have a backpack, and so on.

行人属性识别

是一个多标签图像分类问题
https://paperswithcode.com/task/pedestrian-attribute-recognition/
2022.1.3

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一、数据集

1.PA-100K

PA-100K is a recent-proposed large pedestrian attribute dataset, with 100,000 images in total collected from outdoor surveillance cameras. It is split into 80,000 images for the training set, and 10,000 for the validation set and 10,000 for the test set. This dataset is labeled by 26 binary attributes. The common features existing in both selected dataset is that the images are blurry due to the relatively low resolution and the positive ratio of each binary attribute is low.

2.PETA

The PEdesTrian Attribute dataset (PETA) is a dataset fore recognizing pedestrian attributes, such as gender and clothing style, at a far distance. It is of interest in video surveillance scenarios where face and body close-shots and hardly available. It consists of 19,000 pedestrian images with 65 attributes (61 binary and 4 multi-class). Those images contain 8705 persons.

3.RAP

The Richly Annotated Pedestrian (RAP) dataset is a dataset for pedestrian attribute recognition. It contains 41,585 images collected from indoor surveillance cameras. Each image is annotated with 72 attributes, while only 51 binary attributes with the positive ratio above 1% are selected for evaluation. There are 33,268 images for the training set and 8,317 for testing.

4.UAV-Human

UAV-Human: A Large Benchmark for Human Behavior Understanding with
Unmanned Aerial Vehicles
UAV-Human is a large dataset for human behavior understanding with UAVs. It contains 67,428 multi-modal video sequences and 119 subjects for action recognition, 22,476 frames for pose estimation, 41,290 frames and 1,144 identities for person re-identification, and 22,263 frames for attribute recognition. The dataset was collected by a flying UAV in multiple urban and rural districts in both daytime and nighttime over three months, hence covering extensive diversities w.r.t subjects, backgrounds, illuminations, weathers, occlusions, camera motions, and UAV flying attitudes. This dataset can be used for UAV-based human behavior understanding, including action recognition, pose estimation, re-identification, and attribute recognition.

二、Paper and Code

1.Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method

25 May 2020 · Jian Jia, Houjing Huang, Wenjie Yang, Xiaotang Chen, Kaiqi Huang

Model——StrongBaseline

Code——https://github.com/valencebond/Rethinking_of_PAR

2.Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization

ICCV 2019 · Chufeng Tang, Lu Sheng, Zhao-Xiang Zhang, Xiaolin Hu ·

Model——Attribute-Specific Localization

Code——https://github.com/chufengt/iccv19_attribute

3.HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis

ICCV 2017 · Xihui Liu, Haiyu Zhao, Maoqing Tian, Lu Sheng, Jing Shao, Shuai Yi, Junjie Yan, Xiaogang Wang

Model——HP-net

Code——https://github.com/xh-liu/HydraPlus-Net

三、相关Github开源项目

https://github.com/lanmengyiyu/yolov5-deepmar
https://github.com/dangweili/pedestrian-attribute-recognition-pytorch
https://github.com/yuange250/video_pedestrian_attributes_recognition
https://github.com/hyk1996/Person-Attribute-Recognition-MarketDuke