Author name cluster

Young-Ho Park

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3 papers

1 author row

AAAI Conference 2020 Short Paper

An Automatic Shoplifting Detection from Surveillance Videos (Student Abstract)

U-Ju Gim
Jae-Jun Lee
Jeong-Hun Kim
Young-Ho Park
Aziz Nasridinov

The use of closed circuit television (CCTV) surveillance devices is increasing every year to prevent abnormal behaviors, including shoplifting. However, damage from shoplifting is also increasing every year. Thus, there is a need for intelligent CCTV surveillance systems that ensure the integrity of shops, despite workforce shortages. In this study, we propose an automatic detection system of shoplifting behaviors from surveillance videos. Instead of extracting features from the whole frame, we use the Region of Interest (ROI) optical- ﬂow fusion network to highlight the necessary features more accurately.

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AAAI Conference 2019 Short Paper

A Feasibility Test on Preventing PRMDs Based on Deep Learning

So-Hyun Park
Sun-Young Ihm
Aziz Nasridinov
Young-Ho Park

This study proposes a method to reduce the playing-related musculoskeletal disorders (PRMDs) that often occur among pianists. Specifically, we propose a feasibility test that evaluates several state-of-the-art deep learning algorithms to prevent injuries of pianist. For this, we propose (1) a C3P dataset including various piano playing postures and show (2) the application of four learning algorithms, which demonstrated their superiority in video classification, to the proposed C3P datasets. To our knowledge, this is the first study that attempted to apply the deep learning paradigm to reduce the PRMDs in pianist. The experimental results demonstrated that the classification accuracy is 80% on average, indicating that the proposed hypothesis about the effectiveness of the deep learning algorithms to prevent injuries of pianist is true.

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AAAI Conference 2019 Short Paper

AVS-Net: Automatic Visual Surveillance Using Relation Network

Sein Jang
Young-Ho Park
Aziz Nasridinov

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.

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