Automated Crowd Controlling System Using Image Processing and Video Processing Technique to Avoid Stamped

Automated Crowd Controlling System Using Image Processing and Video Processing Technique to Avoid Stamped

Syeda Ruheena Quadri (Maulana Azad College, Aurangabad, India)
Copyright: © 2019 |Pages: 8
DOI: 10.4018/IJAEC.2019070103

Abstract

Crowd control is needed to prevent the outbreak of disorder and prevent possible stampedes. An automated detection of people crowds from images has become a very important research field. Due to the importance of the topic, many researchers tried to solve this problem using CCTV street cameras. There are still significant problems in managing public pedestrian transport areas such as railway stations, stadiums, shopping malls, and religious gatherings. Using CCTV cameras, some image processing techniques are suitable for an automatic crowd monitoring system. The feasibility of such a system has been tested by analyzing the crowd behavior, crowd density and motion. Traditional measurement techniques, based on manual observations, are not suitable for comprehensive data collection of patterns of density and movement. Real-time monitoring is tedious and tiring, but critical for safety. The author has investigated a number of techniques for crowd density estimation, movement estimation, incident detection and their merits using image processing.
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1. Introduction

Although crowds are made up of impartial people, every with their personal goals and behavior styles, the behaviour of crowds is broadly understood to have collective traits which may be described in popular terms. For example, descriptions consisting of ‘an indignant crowd’, a ‘non violent crowd’ and so forth. Are properly general. The use of phrases which includes “mob”, “mob rule”, have as a minimum considering Roman instances, carried an implication that a crowd is something apart from the sum of its character parts and that it is able to own behaviour styles which vary from the behaviour expected in my view from its participants. It seems the cruelest and most unnecessary of deaths - to be crushed in the midst of a crowd. But even in the 21st Century such deaths are still common, as a stampede at a recent Hindu festival in India, which killed about 115 people, proved all too sadly. Horror quickly turned to anger as the Indian media reported that better crowd management could have prevented the tragedy. It might be assumed that if there is some scientific foundation for the look at of crowd behaviour, it ought to belong well within the social sciences and psychology, and that the bodily sciences and engineering haven’t any business in getting concerned with such studies. However, digital engineers have the know-how of discipline theory and of waft dynamics which may also offer insight into characteristics of crowd behaviour and may also provide the information to signify solutions to crowd monitoring and manipulate based on technological traits in image processing and image knowledge. Technology also plays a role in making sure that such disasters are not repeated. Although there has been a rapid increase in the use of monitoring systems using closed circuit television (CCTV) in public transport areas such as underground stations, railway stations, airports and religious gathering; there are still significant problems in managing such facilities. Installations might have tens or even hundreds of video cameras, but not all these can be monitored at the same time, as the number of observers is usually limited. Therefore, some incidents can be missed or only detected after considerable delays. Potentially dangerous situations are fortunately relatively rare and human operators are subjected to long uneventful sessions. This could lead to lack of concentration at critical periods. When quantitative data needs to be gathered for long periods of time, manual techniques are too costly, unreliable or inaccurate. Therefore, it is necessary to develop automated techniques for acquisition and analysis of crowd behaviour to control the unwanted accidents. Ideally, such techniques should work using existing CCTV systems.

The major areas where automated analysis could have significant impacts are:

  • On-line monitoring for incident detection;

  • Accurate data gathering and analysis;

  • Processing on the analysed data.

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2. Image Processing

Image processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. It is a type of signal dispensation in which input is image, like video frame or photograph and output may be image or characteristics associated with that image. Usually Image Processing system includes treating images as two-dimensional signals while applying already set signal processing methods to them.

To require image processing and computer vision techniques to match such capabilities of human observers is at present unrealistic. However, study of the methods used by human observers may help in the choice of image processing algorithms likely to be useful in automatic assessment of crowd behavior.

It is among rapidly growing technologies today, with its applications in various aspects of a business. Image Processing forms core research area within engineering and computer science disciplines too.

Image processing basically includes the following three steps:

  • Importing image with optical scanner or by digital photography;

  • Analysing and manipulating the image, which includes data compression and image enhancement and potting patterns, that is not to human eyes like satellite photographs;

  • Output is the last stage in which result can be altered image or report that is based on image analysis.

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