NDLI: Beyond counting: point supervised attention guided neural network for crowded object locating

Content Provider	IET Digital Library
Author	Zhang, Kuncai Lu, Zhe Ming Feng, Ya Pei
Abstract	Recent advances in convolutional neural networks have achieved prominent results for object counting or locating in images. In these networks, some adopt detection-based methods which require providing bounding-box annotations for training and these methods usually cannot deal with the crowded cases well. Other networks adopt density regression-based methods which require only point annotations for training but these methods can only predict the total count without providing any location information. In this Letter, the authors propose an attention guided convolutional neural network (AGNet) for both crowded object locating and counting using point annotation as supervision without annotated bounding boxes which are far more time-consuming to label. Accordingly, they define a multi-task objective loss function to lead the training of AGNet to achieve the goal of crowded object locating. The experimental results on four challenging benchmark datasets for crowded objects counting demonstrate the superiority of the proposed AGNet over recent state-of-the-art approaches.
Starting Page	1039
Ending Page	1041
Page Count	3
ISSN	00135194
Volume Number	55
e-ISSN	1350911X
Issue Number	Issue 19, Sep (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/55/19
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2019.2161
Journal	Electronics Letters
Publisher Date	2019-07-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Annotated Bounding Boxes Bounding-box Annotations Computer Vision And Image Processing Technique Convolutional Neural Network Counting Using Point Annotation Crowded Cases Crowded Object Locating Crowded Objects Density Regression-based Method Detection-based Method Image Representation Knowledge Engineering Technique Learning in AI Location Information Multitask Objective Loss Function Neural Computing Technique Neural Nets Object Detection Object Tracking Optical, Image And Video Signal Processing Point Annotations Point Supervised Attention Regression Analysis Statistics Total Count
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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