Fashion Apparel Detection: The Role of Deep Convolutional Neural Network and Pose-dependent Priors

Abstract

In this work, we propose and address a new computer vision task, which we call fashion item detection, where the aim is to detect various fashion items a person in the image is wearing or carrying. The types of fashion items we consider in this work include hat, glasses, bag, pants, shoes and so on. The detection of fashion items can be an important first step of various e-commerce applications for fashion industry. Our method is based on state-of-the-art object detection method pipeline which combines object proposal methods with a Deep Convolutional Neural Network. Since the locations of fashion items are in strong correlation with the locations of body joints positions, we incorporate contextual information from body poses in order to improve the detection performance. Through the experiments, we demonstrate the effectiveness of the proposed method.

Accepted for publication at IEEE Winter Conference on Applications of Computer Vision (WACV) 2016

References in corpus (1)

• Unified Structured Learning for Simultaneous Human Pose Estimation and Garment Attribute Classification

Cited by in corpus (4)

• Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification
• ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations
• Detection of Customer Interested Garments in Surveillance Video using Computer Vision
• Holi-DETR: Holistic Fashion Item Detection Leveraging Contextual Information