[Paper Review] Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization

Wei‐Chen Chen, Xinyi Yu+1 more|arXiv (Cornell University)|Jun 11, 2021

Video Surveillance and Tracking Methods52 references4 citations

TL;DR

This paper proposes View-attribute Attention Localization (VALA), a novel method for pedestrian attribute recognition in video surveillance that leverages view-specific attention and regional attention to improve attribute localization and recognition accuracy. By integrating view prediction with attention mechanisms, VALA achieves state-of-the-art performance on RAP, RAPv2, and PA-100K datasets, demonstrating superior localization and recognition capabilities through joint view and spatial attention supervision.

ABSTRACT

Pedestrian attribute recognition in surveillance scenarios is still a challenging task due to the inaccurate localization of specific attributes. In this paper, we propose a novel view-attribute localization method based on attention (VALA), which utilizes view information to guide the recognition process to focus on specific attributes and attention mechanism to localize specific attribute-corresponding areas. Concretely, view information is leveraged by the view prediction branch to generate four view weights that represent the confidences for attributes from different views. View weights are then delivered back to compose specific view-attributes, which will participate and supervise deep feature extraction. In order to explore the spatial location of a view-attribute, regional attention is introduced to aggregate spatial information and encode inter-channel dependencies of the view feature. Subsequently, a fine attentive attribute-specific region is localized, and regional weights for the view-attribute from different spatial locations are gained by the regional attention. The final view-attribute recognition outcome is obtained by combining the view weights with the regional weights. Experiments on three wide datasets (RAP, RAPv2, and PA-100K) demonstrate the effectiveness of our approach compared with state-of-the-art methods.

Motivation & Objective

• To address the challenge of inaccurate attribute localization in pedestrian attribute recognition under surveillance conditions.
• To improve recognition performance by leveraging multi-view information and attention mechanisms to focus on relevant body regions.
• To develop a unified framework that jointly optimizes view estimation and attribute localization through attention supervision.
• To enhance feature representation by modeling inter-channel dependencies and spatial attention for attribute-specific regions.

Proposed method

• A view prediction branch generates four view weights representing confidence in attributes from different perspectives (front, back, left, right).
• View weights are fused with feature maps to form view-attribute representations that guide deep feature extraction with attention supervision.
• Regional attention is applied to encode spatial dependencies and inter-channel relationships in view-specific features to localize attribute-relevant regions.
• Spatially aware regional weights are generated via regional attention, refining the localization of attribute-specific body parts.
• Final attribute recognition is achieved by combining view weights and regional weights through a weighted fusion mechanism.
• The model is trained end-to-end on three benchmark datasets: RAP, RAPv2, and PA-100K, with supervision from both view and attribute localization.

Experimental results

Research questions

• RQ1 How can multi-view information be effectively utilized to improve attribute localization in pedestrian recognition?
• RQ2 Can attention mechanisms enhance the localization of attribute-specific body regions in surveillance videos?
• RQ3 To what extent does joint supervision from view prediction and regional attention improve recognition accuracy?
• RQ4 How does the proposed view-attribute attention mechanism compare to conventional attention or localization methods on standard benchmarks?

Key findings

• VALA achieves state-of-the-art performance on the RAP, RAPv2, and PA-100K datasets, outperforming existing methods in attribute recognition accuracy.
• The integration of view weights and regional attention significantly improves localization precision of attribute-relevant body parts.
• The view prediction branch enhances model robustness by providing confidence-aware guidance for attribute recognition across different pedestrian views.
• The ablation study confirms that both view supervision and regional attention contribute meaningfully to performance gains.
• The method demonstrates strong generalization across diverse surveillance scenarios due to its attention-based localization and view-aware feature learning.