학술논문

Multi-glimpse LSTM with color-depth feature fusion for human detection
Document Type
Conference
Source
2017 IEEE International Conference on Image Processing (ICIP) Image Processing (ICIP), 2017 IEEE International Conference on. :905-909 Sep, 2017
Subject
Computing and Processing
Signal Processing and Analysis
Proposals
Image color analysis
Feature extraction
Logic gates
Training
Context modeling
Fuses
Human detection
RGB-D
LSTM
Feature fusion
Language
ISSN
2381-8549
Abstract
With the development of depth cameras such as Kinect and Intel Realsense, RGB-D based human detection receives continuous research attention due to its usage in a variety of applications. In this paper, we propose a new Multi-Glimpse LSTM (MG-LSTM) network, in which multi-scale contextual information is sequentially integrated to promote the human detection performance. Furthermore, we propose a feature fusion strategy based on our MG-LSTM network to better incorporate the RGB and depth information. To the best of our knowledge, this is the first attempt to utilize LSTM structure for RGB-D based human detection. Our method achieves superior performance on two publicly available datasets.