BROWSE
Detail
Conference Paper
GSR-MAR: Global Super-Resolution for Person Multi-Attribute Recognition
Cited 4 time in 
Share 
Share
- Authors
- Thomhert S. Siadari, Mikyong Han, Hyunjin Yoon
- Issue Date
- 2019-10
- Citation
- International Conference on Computer Vision Workshops (ICCVW) 2019, pp.1098-1103
- Language
- English
- Type
- Conference Paper
- Abstract
- Person attribute recognition aims to predict attribute labels based on person's appearance usually captured from surveillance cameras. It is a challenging problem in computer vision due to poor imaging quality with complex background clutter and unconstrained viewing conditions from various angles and distances between person and surveillance cameras. In this paper, we address such a problem using an end-to-end network called Global Super-Resolution for Multi Attribute Recognition (GSR-MAR). GSR-MAR integrates a conversion process of low-resolution input images into high-resolution images and predicts person attributes from input images. Before performing the classification process, GSR-MAR not only converts low-resolution images to high-resolution images to recover details of image textures but also captures larger context information by using large separable convolutional layers. The experiment results on two popular benchmark datasets demonstrate the performance improvement and effectiveness of our GSR-MAR model over competing baselines.
- KSP Keywords
- Attribute recognition, Background clutter, Benchmark datasets, Classification process, Complex background, Computer Vision(CV), Context Information, End to End(E2E), Experiment results, Low-resolution images, Super resolution