Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization

• URL: http://arxiv.org/abs/2106.06485v1
• Date: Fri, 11 Jun 2021 16:09:31 GMT
• Title: Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization
• Authors: Weichen Chen (1) Xinyi Yu (1) Linlin Ou (1) ((1) Collage of Information Engineering, Zhejiang University of Technology, Hangzhou, China)
• Abstract summary: We propose a novel view-attribute localization method based on attention (VALA) A specific view-attribute is composed by the extracted attribute feature and four view scores which are predicted by view predictor as the confidences for attribute from different views. Experiments on three wide datasets (RAP, RAPv2, PETA, and PA-100K) demonstrate the effectiveness of our approach compared with state-of-the-art methods.
• Score: 8.807717261983539
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pedestrian attribute recognition in surveillance scenarios is still a challenging task due to inaccurate localization of specific attributes. In this paper, we propose a novel view-attribute localization method based on attention (VALA), which relies on the strong relevance between attributes and views to capture specific view-attributes and to localize attribute-corresponding areas by attention mechanism. A specific view-attribute is composed by the extracted attribute feature and four view scores which are predicted by view predictor as the confidences for attribute from different views. View-attribute is then delivered back to shallow network layers for supervising deep feature extraction. To explore the location of a view-attribute, regional attention is introduced to aggregate spatial information of the input attribute feature in height and width direction for constraining the image into a narrow range. Moreover, the inter-channel dependency of view-feature is embedded in the above two spatial directions. An attention attribute-specific region is gained after fining the narrow range by balancing the ratio of channel dependencies between height and width branches. The final view-attribute recognition outcome is obtained by combining the output of regional attention with the view scores from view predictor. Experiments on three wide datasets (RAP, RAPv2, PETA, and PA-100K) demonstrate the effectiveness of our approach compared with state-of-the-art methods.

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