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Sein Jang

dblp:245/3390 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Image recognition and object detection · 50% Video understanding and tracking · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection
object detection		 0.412019
AVS-Net: Automatic Visual Surveillance Using Relation Network · AAAI 2019
Computer vision › Video understanding and tracking
video surveillance		 0.412019
AVS-Net: Automatic Visual Surveillance Using Relation Network · AAAI 2019

Methods — techniques the papers use, named apart from their topics

relation network · 0.4
YearPublicationVenuePosition
2019 AVS-Net: Automatic Visual Surveillance Using Relation Network
abstract
Visual surveillance through closed circuit television (CCTV) can help to prevent crime. In this paper, we propose an automatic visual surveillance network (AVS-Net), which simultaneously performs image processing and object detection to determine the dangers of situations captured by CCTV. In addition, we add a relation module to infer the relationships of the objects in the images. Experimental results show that the relation module greatly improves classification accuracy, even if there is not enough information.
Sein Jang, Young-Ho Park 0002, Aziz Nasridinov
 AAAI1