Improve the Performance of People Detection using Fisher Linear Discriminant Analysis in Surveillance

서베일런스에서 피셔의 선형 판별 분석을 이용한 사람 검출의 성능 향상

  • Kang, Sung-Kwan (HCI Lab., Department of Computer and Information Engineering, Inha University) ;
  • Lee, Jung-Hyun (Department of Computer and Information Engineering, Inha University)
  • Received : 2013.10.22
  • Accepted : 2013.12.20
  • Published : 2013.12.28


Many reported methods assume that the people in an image or an image sequence have been identified and localization. People detection is one of very important variable to affect for the system's performance as the basis technology about the detection of other objects and interacting with people and computers, motion recognition. In this paper, we present an efficient linear discriminant for multi-view people detection. Our approaches are based on linear discriminant. We define training data with fisher Linear discriminant to efficient learning method. People detection is considerably difficult because it will be influenced by poses of people and changes in illumination. This idea can solve the multi-view scale and people detection problem quickly and efficiently, which fits for detecting people automatically. In this paper, we extract people using fisher linear discriminant that is hierarchical models invariant pose and background. We estimation the pose in detected people. The purpose of this paper is to classify people and non-people using fisher linear discriminant.


Supported by : 인하대학교


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