Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Towards Real-time Multi-object Tracking in CPU Environment

CPU 환경에서의 실시간 동작을 위한 딥러닝 기반 다중 객체 추적 시스템

  • Kim, Kyung Hun (Dept. of Electronic Engineering, Sogang University) ;
  • Heo, Jun Ho (Dept. of Electronic Engineering, Sogang University) ;
  • Kang, Suk-Ju (Dept. of Electronic Engineering, Sogang University)
  • Received : 2020.01.14
  • Accepted : 2020.03.03
  • Published : 2020.03.30
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Abstract

Recently, the utilization of the object tracking algorithm based on the deep learning model is increasing. A system for tracking multiple objects in an image is typically composed of a chain form of an object detection algorithm and an object tracking algorithm. However, chain-type systems composed of several modules require a high performance computing environment and have limitations in their application to actual applications. In this paper, we propose a method that enables real-time operation in low-performance computing environment by adjusting the computational process of object detection module in the object detection-tracking chain type system.

최근 딥러닝 모델을 기반으로 한 객체 추적 알고리즘의 활용도가 증가하고 있다. 영상에서의 다중 객체의 추적을 위한 시스템은 대표적으로 객체 검출 알고리즘과 객체 추적 알고리즘의 연쇄된 형태로 구성되어있다. 하지만 여러 모듈로 구성된 연쇄 형태의 시스템은 고성능 컴퓨팅 환경을 요구하며 실제 어플리케이션으로의 적용에 제한사항으로 존재한다. 본 논문에서는 위와 같은 객체 검출-추적의 연쇄 형태의 시스템에서 객체 검출 모듈의 연산 관련 프로세스를 조정하여 저성능 컴퓨팅 환경에서도 실시간 동작을 가능하게 하는 방법을 제안한다.

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