NDLI: Pose detection in complex classroom environment based on improved Faster R-CNN

Content Provider	IET Digital Library
Author	Tang, Lin Gao, Chenqiang Chen, Xu Zhao, Yue
Abstract	Pose detection of small targets in poor imaging conditions like heavy occlusion and low resolution is still an open and challenging task in computer vision. For instance, detection of students' poses in classrooms that are even indistinguishable to human eyes remains a rather difficult task. Motivated by the success of convolutional feature merging and locality preserving, the authors propose a pose detection framework combining merged region of interest (ROI) pooling and locality preserving learning. Unlike usual object detection algorithms which use general top-level convolutional features as inputs, their method uses a merged ROI pooling structure to merge semantic feature and high-resolution feature from the last two levels of convolutional feature maps, so that this merged feature is made more expressive than the single-level feature. In addition, the locality feature-preserving learning is used in the last fully-connected layer. Through locality preserving learning, features belonging to the same class would be forced to be closer in the feature space, which enables the model with stronger classification ability. Experimental results show that the proposed method outperforms the state-of-the-art methods.
Starting Page	451
Ending Page	457
Page Count	7
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 3, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5905
Journal	IET Image Processing
Publisher Date	2018-11-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Complex Classroom Environment Computer Vision Computer Vision And Image Processing Technique Convolutional Feature Maps Convolutional Feature Merging Feature Extraction General Top-level Convolutional Feature High-resolution Feature Image Classification Image Recognition Image Resolution Imaging Condition Improved Faster R-CNN Knowledge Engineering Technique Learning in AI Locality Feature-preserving Learning Merged ROI Pooling Structure Neural Computing Technique Neural Nets Object Detection Object Detection Algorithm Occlusion Pose Detection Region of Interest Semantic Feature
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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