Based on Wide Area Environment Abnormal Behavior Analysis and Anomaly Detection Research

X Qiu,Lin Zhang,W.A Yusmawiza,A.J Arumugham,M Kocisko,R.K Goyal,Xiaoping Li,M.M Ulkhaq

doi:10.1051/matecconf/20166810006

Abstract

Group anomaly identification and location is an important issue in the field of artificial intelligence. Capture of the accident source and rapid prediction of mass incidents in public places are difficult problems in intelligent video identification and processing, but the traditional group anomaly detection research has many limitations when it comes to accident source detection and intelligent recognition. We are to research on the algorithms of accident source location and abnormal group identification based on behavior analysis in the condition of dramatically changing group geometry appearance, including: 1) to propose a logic model of image density based on the social force model, and to build the crowd density trend prediction model integrating “fast and fuzzy matching at front-end” and “accurate and classified training at back-end”; 2) to design a fast abnormal source flagging algorithm based on support vector machine, and to realize intelligent and automatic marking of abnormal source point; 3) to construct a multi-view human body skeleton invariant moment model and a motion trajectory model based on linear parametric equations. The expected results of the research will help prevent abnormal events effectively, capture the first scene of incidents and the abnormal source point quickly, and play a decision support role in the proactive national security strategy.

Highlights

Some large-scale unexpected mass incidents occur more frequently in public area with the development of society and economy and growth of population density
This paper is intended to study the detection of abnormal source points of the wide area group and the determination of the nature of the source points based on the understanding and analysis of the intelligent video, try to make breakthroughs on abnormal population model, abnormal source detection and location, the source of abnormal attribute analysis, model training and matching, camera collaborative scheduling key technology
In order to achieve population density trends prediction of according to the "fast fuzzy matching" and "accurate classification of training", group trend prediction should be combined with social force model and population density trend model should be built, anomaly source should be focused by multi stage split control strategy to capture the first scene of the abnormal events of the source point timely and predict the trend of group events rapidly

Summary

Introduction

Some large-scale unexpected mass incidents occur more frequently in public area with the development of society and economy and growth of population density These things will lead to serious consequences, if we can’t capture the first scene and the source point quickly and do the rapid early warning. Rapid propagation of new abnormal group model, multi region and multi scene linkage realtime warning should be achieved by continuous training "group trend - the source of the object behavior attribute" relationship, determining attribute of abnormal source and pushing analysis on the attribute of the anomaly source This will make a positive contribution to the early warning of public places

Analysis of group event types

Analysis of anomaly source location

Analysis of the behavior of abnormal objects

Program architecture design

Conclusions