Real-Time Object Tracking Algorithm based on Pattern Classification in Surveillance Networks

서베일런스 네트워크에서 패턴인식 기반의 실시간 객체 추적 알고리즘

  • Kang, Sung-Kwan (HCI Lab., Department of Computer and Information Engineering, Inha University) ;
  • Chun, Sang-Hun (Department of Information and Technology, Incheon JEI University)
  • Received : 2015.12.30
  • Accepted : 2016.02.20
  • Published : 2016.02.28


This paper proposes algorithm to reduce the computing time in a neural network that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. Object Detection can be defined as follows : Given image sequence, which can forom a digitalized image, the goal of object detection is to determine whether or not there is any object in the image, and if present, returns its location, direction, size, and so on. But object in an given image is considerably difficult because location, size, light conditions, obstacle and so on change the overall appearance of objects, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact object detection which overcomes some restrictions by using neural network. Proposed system can be object detection irrelevant to obstacle, background and pose rapidly. And neural network calculation time is decreased by reducing input vector size of neural network. Principle Component Analysis can reduce the dimension of data. In the video input in real time from a CCTV was experimented and in case of color segment, the result shows different success rate depending on camera settings. Experimental results show proposed method attains 30% higher recognition performance than the conventional method.


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