Merging Super Resolution and Attribute Learning for Low-Resolution Person Attribute Recognition

Abstract

In video surveillance, visual person attributes such as gender, backpack, type of clothing are crucial for person search or re-identification. For detecting and retrieving these attributes with high accuracy, the availability of high-quality videos is a necessity in general. However, this cannot be guaranteed in general surveillance videos or images; beside improving hardware technology, improving inference algorithms on low-resolution data is valuable. This paper attempts to propose two solutions in this direction: designing a combined neural network architecture from existing architectures, and a novel combination approach toward re-identification on low-resolution videos. The proposed architecture introduces a combined Neural Network architecture, called SRMAR, that jointly trains Super Resolution and Multi Attribute Recognition models for more effective recognition. Experiments on two benchmark datasets demonstrate the effectiveness and applicability of the proposed neural network architecture for low-resolution multi-attribute recognition. Furthermore, a higher-level linear combination scheme that optimally combines the proposed SRMAR architecture and multi-attribute recognition network is presented, yielding superior results in low-resolution person attribute recognition.

Keywords

• Attribute recognition
• linear programming
• linear combination
• neural networks
• parametric inference
• super resolution