Earticle

초록

영어

In the area of crowd abnormal detection, the parameter of population density, is seldom used to the global crowd behavior detection. Some of the references simply use the LBP or spatial-temporal LBP features to fulfill the abnormal detection. They don’t make full use of the crowd density characteristics and dynamic characteristics. This paper proposes a novel method by increasing the dimension of feature vector to increase the information content so as to improve the recognition accuracy. That is to say the crowd dynamic information and crowd density information will be combined together to form a higher dimension of feature vectors, which is named as the crowd behavior feature vector in this paper to improve the robustness of the algorithm. Finally, Support Vector Machines (SVM) is adopted to detect the abnormal events using the crowd behavior feature vector. This work utilizes the Local Binary Pattern Co-Occurrence Matrix (LBPCM) for crowd density estimation to ensure the excellent accuracy. At the same time, it adopts high accuracy optical flow histograms of the orientation of interaction force to extract the crowd dynamic information (HOIF). After verification, we discovered this algorithm not only can get the good discrimination on the benchmark dataset UMN, but also can achieve the pretty high recognition rate about the web dataset.

목차

Abstract
 1. Introduction
 2. Global Abnormal Crowd Behavior Detection
  2.1 Crowd Motion Detection
  2.2. Histograms of the Orientation of Interaction Force
  2.3. Local Binary Pattern Co-Occurrence Matrix
 3. Global Abnormal Crowd Behavior Detection
  3.1. UMN Dataset
  3.2. Web Dataset
 4. Conclusions
 References

키워드

저자

• Yong Yin [ College of Communication Engineering, Chongqing University, Chongqing 400044, China ] Corresponding author
• Qiannan Liu [ College of Communication Engineering, Chongqing University, Chongqing 400044, China ]
• Shibiao Mao [ College of Communication Engineering, Chongqing University, Chongqing 400044, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605