Abnormal Crowd Behavior Detection and Localization via Kernel Based Direct Density Ratio Estimation

• Main Authors: Chih-Yuan Lee, 李治原
• Other Authors: Wen-Hsien Fang
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/95546677836042558374

碩士 === 國立臺灣科技大學 === 電子工程系 === 102 === In this theme, we consider the analysis of abnormally behavior in surveillance system. To simplify the problem, we formalized it as an outlier detection problem. In our case, all behaviors in training data are normal. By creating a model by training data, we can define abnormalities whose probability is below a certain threshold under this model. Based on this, we use Kullback–Leibler importance estimation procedure (KLIEP) to compute the ratio of training data and testing data which we used as our inlier score. The KLIEP is a method to estimate the inlier score, not the probability densities themselves. This formulation allows us to avoid non-parametric density estimation, which is known to be a difficult task. After computing inlier score by KLIEP, we create a model by training data and testing data. According to this model, other testing data can also get a inlier score which can represent the degree of similarity between training data and testing data. Based on the concept of inlier-based outlier detection and normal score, we can determine an appropriate threshold and find which location at testing data is abnormal. In our evaluation we used PASCAL metric to evaluate our localization rate by ground truth. Through computer simulations, we find that our method has high accuracy of localization rate in UCSD dataset compared with previous works.