An Attention-Based Deep Learning Model for Multiple Pedestrian Attributes Recognition

• URL: http://arxiv.org/abs/2004.01110v1
• Date: Thu, 2 Apr 2020 16:21:14 GMT
• Title: An Attention-Based Deep Learning Model for Multiple Pedestrian Attributes Recognition
• Authors: Ehsan Yaghoubi, Diana Borza, Jo\~ao Neves, Aruna Kumar, Hugo Proen\c{c}a
• Abstract summary: This paper provides a novel solution to the problem of automatic characterization of pedestrians in surveillance footage. We propose a multi-task deep model that uses an element-wise multiplication layer to extract more comprehensive feature representations. Our experiments were performed on two well-known datasets (RAP and PETA) and point for the superiority of the proposed method with respect to the state-of-the-art.
• Score: 4.6898263272139795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The automatic characterization of pedestrians in surveillance footage is a tough challenge, particularly when the data is extremely diverse with cluttered backgrounds, and subjects are captured from varying distances, under multiple poses, with partial occlusion. Having observed that the state-of-the-art performance is still unsatisfactory, this paper provides a novel solution to the problem, with two-fold contributions: 1) considering the strong semantic correlation between the different full-body attributes, we propose a multi-task deep model that uses an element-wise multiplication layer to extract more comprehensive feature representations. In practice, this layer serves as a filter to remove irrelevant background features, and is particularly important to handle complex, cluttered data; and 2) we introduce a weighted-sum term to the loss function that not only relativizes the contribution of each task (kind of attributed) but also is crucial for performance improvement in multiple-attribute inference settings. Our experiments were performed on two well-known datasets (RAP and PETA) and point for the superiority of the proposed method with respect to the state-of-the-art. The code is available at https://github.com/Ehsan-Yaghoubi/MAN-PAR-.

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