Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Building Method an Image Dataset for Tracking Objects in a Video

동영상 내 객체 추적을 위한 영상 데이터셋 구축 방법

  • Kim, Ji-Seong (Department of Computer Engineering, Dong-Eui University) ;
  • Heo, Gyeongyong (Department of Electronic Engineering, Dong-eui University) ;
  • Jang, Si-Woong (Department of Computer Engineering, Dong-Eui University)
  • Received : 2021.10.21
  • Accepted : 2021.11.18
  • Published : 2021.12.31
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Abstract

A large amount of image data sets are required for image deep learning, and there are many differences in the method of obtaining images and constructing image data sets depending on the type of object. In this paper, we presented a method of constructing an image data set for deep learning and analyzed the performance that varies depending on the object to be tracked. We took a video by rotating the object, and then created a data set by segmenting the video using the proposed data set construction method. As a result of performance analysis, detection rate was more than 95%, and detection rate of objects with little change in shape was higher performance. It is considered that it is effective to use the data set construction method presented in this paper for a situation in which it is difficult to obtain image data and to track an object with little change in shape within a video.

영상 딥러닝을 위해서는 다량의 영상 데이터셋이 필요한데, 객체의 종류에 따라 영상을 구하고 영상 데이터셋을 구축하는 방법에 많은 차이가 있다. 본 논문에서는 딥러닝을 위한 영상 데이터셋을 구축하는 방법을 제시하고 추적하는 객체에 따라 달라지는 성능을 분석하였다. 제안하는 데이터셋 구축방법을 활용하여 객체를 회전시킨 후 동영상을 촬영, 분할하여 커스텀 데이터셋을 구축하고, 성능을 분석한 결과 95% 이상의 객체 검출률을 보였으며, 이동 시 객체의 형상 변화가 적은 경우에 더 높은 성능이 나타났다. 영상 데이터를 구하기 어렵고, 형태의 변화가 적은 객체를 동영상 내에서 추적하기 위한 상황을 위하여는 본 논문에서 제시한 데이터셋 구축방법을 활용하는 것이 효과적일 것으로 판단된다.

Keywords

Acknowledgement

This research was supported by the MSIT (Ministry of Science and ICT),Korea, under the Grand Information Technology Research Center support program(IITP-2021-2020-0-01791) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation)

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