Journal
IEEE ACCESS
Volume 11, Issue -, Pages 10881-10893Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2023.3241334
Keywords
Transformers; Feature extraction; Convolutional neural networks; Visualization; Location awareness; Image recognition; Deep learning; Attribute recognition; CNN; deep neural network; image classification; transformers
Ask authors/readers for more resources
This paper presents a method applied to networks of primary convolutional neurons to locate the area connected to the Person attribute. By using Individual Feature Identification, the features of a person, such as gender, age, fashion sense, and equipment, are focused on in video surveillance analytics. The extensive experimental results demonstrate that the proposed hybrid technique outperforms current strategies on four unique private characteristic datasets.
Numerous deep perception technologies and methods are built on the foundation of pedestrian feature identification. It covers various fields, including autonomous driving, spying, and object tracking. A recent study area is the identification of personality traits that has attracted much interest in video surveillance. Identifying a person's distinct areas is complex and plays an incredibly significant role. This paper presents a current method applied to networks of primary convolutional neurons to locate the area connected to the Person attribute. Using Individual Feature Identification, the features of a person, such as gender, age, fashion sense, and equipment, have received much attention in video surveillance analytics. This Article adopted a Conv-Attentional image transformer that broke down the most discriminating Attribute and region into multiple grades. The feed-forward system and conv-attention are the components of serial blocks, and parallel blocks have two attention-focused tactics: direct cross-layer attention and feature interpolation. It also provides a flexible Attribute Localization Module (ALM) to learn the regional aspects of each Attribute are considered at several levels, and the most discriminating areas are selected adaptively. We draw the conclusion that hybrid transformers outperform pure transformers in this instance. The extensive experimental results indicate that the proposed hybrid technique achieves higher results than the current strategies on four unique private characteristic datasets, i.e., RapV2, RapV1, PETA, and PA100K.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available