A deep learning based multi-color space approach for pedestrian attribute recognition
Faculty / School
Faculty of Computer Sciences (FCS)
Department of Computer Science
Was this content written or created while at IBA?
ICGSP '19: 2019 The 3rd International Conference on Graphics and Signal Processing
1-3 June 2019
Association for Computing Machinery, New York, NY, United States
Abstract / Description
Pedestrian behavior understanding and identification in surveillance scenarios has attraction a tremendous amount of attention over the past many years. An integral part of this problem involves identifying various human visual attributes in the scene. Over the years, researcher have proposed various solutions and have explored various features. However, they have focused on either engineered features or simple RGB images. In this paper, we explore the problem of crowd at- tribute recognition using RGB (Red, Green, Blue), HSV (Hue, Saturation, Value) and L∗A∗B∗ color models and propose a 3-branch Siamese network to solve the problem. We present a unique approach of using these three color models and fine- tune a pre-trained VGG-19 network for our task. We perform extensive experimentation on the most challenging public PETA dataset, which is by far the largest and the most diverse dataset of its kind. We show an improvement over the state of the art work.
Junejo, I. N. (2019). A deep learning based multi-color space approach for pedestrian attribute recognition., 113-116. https://doi.org/10.1145/3338472.3338493