Attention Based CNN-ConvLSTM for Pedestrian Attribute Recognition

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 3; p. 811
• Main Authors: Li, Yang, Xu, Huahu, Bian, Minjie, Xiao, Junsheng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03.02.2020; MDPI
• Subjects: channel attention (catt); Computer vision; conventional long short-term memory (convlstm); convolutional neutral networks (cnn); Correlation; Datasets; Deep learning; Image classification; multi-label classification; Neural networks; Occlusion; pedestrian attribute recognition; Recognition; Semantics; Spatial data; Surveillance; pedestrian attribute recognition; conventional long short-term memory (ConvLSTM); convolutional neutral networks (CNN); multi-label classification; channel attention (CAtt)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20030811

As a result of its important role in video surveillance, pedestrian attribute recognition has become an attractive facet of computer vision research. Because of the changes in viewpoints, illumination, resolution and occlusion, the task is very challenging. In order to resolve the issue of unsatisfactory performance of existing pedestrian attribute recognition methods resulting from ignoring the correlation between pedestrian attributes and spatial information, in this paper, the task is regarded as a spatiotemporal, sequential, multi-label image classification problem. An attention-based neural network consisting of convolutional neural networks (CNN), channel attention (CAtt) and convolutional long short-term memory (ConvLSTM) is proposed (CNN-CAtt-ConvLSTM). Firstly, the salient and correlated visual features of pedestrian attributes are extracted by pre-trained CNN and CAtt. Then, ConvLSTM is used to further extract spatial information and correlations from pedestrian attributes. Finally, pedestrian attributes are predicted with optimized sequences based on attribute image area size and importance. Extensive experiments are carried out on two common pedestrian attribute datasets, PEdesTrian Attribute (PETA) dataset and Richly Annotated Pedestrian (RAP) dataset, and higher performance than other state-of-the-art (SOTA) methods is achieved, which proves the superiority and validity of our method.