PARFormer：基于Transformer的行人属性识别多任务网络 PDF 下载

PARFormer：基于Transformer的行人属性识别多任务网络 PDF 下载

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主要内容：

PETA: It contains a total of 19, 000 pedestrian images

taken by real surveillance cameras [54]. These images are

randomly divided into 9, 500 training images, 1, 900 velidation

images, and 7, 600 test images. Each pedestrian image has

61 binary attributes and 4 multi-category attributes. Because

the distribution of some attributes is very uneven, the existing

methods mainly focus on 35 attributes of the 61 attributes.

PA100K: It is the largest open-source pedestrian attribute

dataset, with 26 pedestrian attributes annotated [55]. It con

tains 100, 000 pedestrian images collected by the surveillance

cameras, with 80, 000 images for training, 10, 000 images for

validation, and 10, 000 images for testing.

RAP: It has two versions, and the RAP-v1 dataset [51] is

used in our experiment. This dataset contains 41, 585 pedes

trian images collected from 26 indoor surveillance cameras,

including 69 binary attributes, while the existing methods

mainly focus on 51 attributes, and each of those is with a

proportion greater than 1%. The training set of this dataset

contains 33, 268 images, and the rest are used for testing.

According to the existing methods, we adopt five metrics

for evaluation: mean average precision (mA), accuracy (Accu),

precision (Prec), recall (Recall), and F1 score (F1)