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The document presents a video-based crowd density estimation system using the Accumulated Mosaic Image Difference (AMID) method. The system extracts crowded areas in monocular video sequences and estimates the number of people and their velocity. Using information from multiple cameras, it can predict crowd density levels several minutes in advance. Experiments in real public spaces like stations, parks and plazas demonstrate the effectiveness of the proposed AMID method for crowd analysis and prediction.
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![TEMPLATE DESIGN 息 2008
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VIDEO BASED CROWD DENSITY ESTIMATION USING AMID METHOD
Vamsi Krishna M(1161010040), G. Srikanth(1161010026) and Trinadh Reddy M(1161010037)
Department of Telecommunication Engineering,
SRM University, Kattankulathur , Chennai, Tamil Nadu-603203, India .
Guided by: Mrs. C. T. Manimegalai, Assistant Professor (S.G)
ABSTRACT
Crowd density estimation in wide areas is a challenging
problem for visual surveillance. Because of the high risk
of degeneration, the safety of public events involving
large crowds has always been a major concern. In this
paper, we propose a video-based crowd density analysis
and prediction system for wide-area surveillance
applications. In monocular image sequences, the
Accumulated Mosaic Image Difference (AMID) method is
applied to extract crowd areas having irregular motion.
The specific number of persons and velocity of a crowd
can be adequately estimated by our system from the
density of crowded areas. Using a multi-camera network,
we can obtain predictions of a crowds density several
minutes in advance. The system has been used in real
applications, and numerous experiments conducted in
real scenes (station, park, plaza) demonstrate the
effectiveness and robustness of the proposed method.
Video analysis techniques are becoming increasingly
popular in the visual surveillance of public areas because
of the great efficiency in gathering information and low
cost in human resource.
Because of the high level of risk crowding has always
been of high concern to relevant authorities.
We propose a crowd analysis system which can be
explored in public areas such as bus stations, subways
and plazas. The degree of crowding is estimated in
monocular image sequences and future crowd densities
and velocities are predicted using the information
obtained from the number of cameras.
A novel method is proposed to estimate the crowd
density through AMID feature. Based on the method a
prediction algorithm is designed which can estimate the
specific number of people and velocity of crowd.
This system also includes crowd density analysis in
images taken by a single camera and crowd density
prediction in images taken by multi cameras. When the
crowd density is obtained from the images taken by
many cameras. We can predict crowd levels in specific
places a few minutes into the future.
There has been an increase in the use of video
surveillance and monitoring in public areas to improve
safety and security.
Probability of density: The total number of people /
area of land
Density: Probability of density*Total no of people
present in an area
INTRODUCTION
FORMALISM AND
COMPUTATIONAL SCHEME
RESULTS AND DISCUSSION
REFERENCES
REALISTIC CONSTRAINTS
[[1
[1] Crowd analysis: a survey B. Zhan 揃 D. N. Monekosso
揃 P. Remagnino April 2008
[2] Jinnian Guo, Xinyu Wu and Tian Cao, Crowd Density
Estimation via Markov Random Field (MRF) May 2010.
[3] Xiaojing Yuan, Zehang Sun, Yaakov Varol, and George
Bebis A Distributed Visual Surveillance System May 2000.
[4] Nikos Paragios & Visvanathan Ramesh, A MRF-based
Approach for Real-Time Subway Monitoring December
2007.
[5] A. Goldsmith, Wireless Communications. Cambridge
University Press, 2005.
From the above graph the x axis represents no of Frames
present in the video Y axis represents no of persons
available for each frame
From the above graph the x axis represents no of frames
and y axis represents crowd density estimation
s1+s2+s3=s4
The above diagram follows the Kirchhoff law the
number of persons entering from a particular area
from three directions (s1,s2,s3) and leaving out in a
single direction(s4)
It is used to calculate the total no of persons in a
particular area
By using AMID method the accuracy rate can be
approximately 90%.
The distance it can be covered is limited to the
coverage area of the camera
CONCLUSION
In this study, we have proposed a crowd density estimation
and prediction system for wide-area security. AMID based
approach is applied to detect crowded areas and a
geometry module is included to correct perspective
distortion. The number of people in a crowd is estimated by
the liner fitting method and the velocity is also obtained by
the optical flow method. After crowd density and velocity are
estimated, the prediction module is used to estimate the
crowd density at designated points at a later time.
Compared to existing methods, the proposed method is a
real time system for applications and the crowd density
analysis algorithm can work properly in both low and high
crowd density scenes. Experiments and real applications
demonstrate the effectiveness and robustness of our
method in real scenes although there are some aspects to
be improved in the system. In the future, we will consider
how to choose the parameter (duration time) adaptation for
different scenes.
Fig1: Block Diagram of Video based crowd
density estimation
Fig2: Diagram for optical flow](https://image.slidesharecdn.com/ed6e9b42-cc32-471b-b757-626f6c98bcd0-151004213418-lva1-app6891/85/poster-presentation-1-320.jpg)
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poster presentation
- 1. TEMPLATE DESIGN 息 2008 www.PosterPresentations.com VIDEO BASED CROWD DENSITY ESTIMATION USING AMID METHOD Vamsi Krishna M(1161010040), G. Srikanth(1161010026) and Trinadh Reddy M(1161010037) Department of Telecommunication Engineering, SRM University, Kattankulathur , Chennai, Tamil Nadu-603203, India . Guided by: Mrs. C. T. Manimegalai, Assistant Professor (S.G) ABSTRACT Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowds density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method. Video analysis techniques are becoming increasingly popular in the visual surveillance of public areas because of the great efficiency in gathering information and low cost in human resource. Because of the high level of risk crowding has always been of high concern to relevant authorities. We propose a crowd analysis system which can be explored in public areas such as bus stations, subways and plazas. The degree of crowding is estimated in monocular image sequences and future crowd densities and velocities are predicted using the information obtained from the number of cameras. A novel method is proposed to estimate the crowd density through AMID feature. Based on the method a prediction algorithm is designed which can estimate the specific number of people and velocity of crowd. This system also includes crowd density analysis in images taken by a single camera and crowd density prediction in images taken by multi cameras. When the crowd density is obtained from the images taken by many cameras. We can predict crowd levels in specific places a few minutes into the future. There has been an increase in the use of video surveillance and monitoring in public areas to improve safety and security. Probability of density: The total number of people / area of land Density: Probability of density*Total no of people present in an area INTRODUCTION FORMALISM AND COMPUTATIONAL SCHEME RESULTS AND DISCUSSION REFERENCES REALISTIC CONSTRAINTS [[1 [1] Crowd analysis: a survey B. Zhan 揃 D. N. Monekosso 揃 P. Remagnino April 2008 [2] Jinnian Guo, Xinyu Wu and Tian Cao, Crowd Density Estimation via Markov Random Field (MRF) May 2010. [3] Xiaojing Yuan, Zehang Sun, Yaakov Varol, and George Bebis A Distributed Visual Surveillance System May 2000. [4] Nikos Paragios & Visvanathan Ramesh, A MRF-based Approach for Real-Time Subway Monitoring December 2007. [5] A. Goldsmith, Wireless Communications. Cambridge University Press, 2005. From the above graph the x axis represents no of Frames present in the video Y axis represents no of persons available for each frame From the above graph the x axis represents no of frames and y axis represents crowd density estimation s1+s2+s3=s4 The above diagram follows the Kirchhoff law the number of persons entering from a particular area from three directions (s1,s2,s3) and leaving out in a single direction(s4) It is used to calculate the total no of persons in a particular area By using AMID method the accuracy rate can be approximately 90%. The distance it can be covered is limited to the coverage area of the camera CONCLUSION In this study, we have proposed a crowd density estimation and prediction system for wide-area security. AMID based approach is applied to detect crowded areas and a geometry module is included to correct perspective distortion. The number of people in a crowd is estimated by the liner fitting method and the velocity is also obtained by the optical flow method. After crowd density and velocity are estimated, the prediction module is used to estimate the crowd density at designated points at a later time. Compared to existing methods, the proposed method is a real time system for applications and the crowd density analysis algorithm can work properly in both low and high crowd density scenes. Experiments and real applications demonstrate the effectiveness and robustness of our method in real scenes although there are some aspects to be improved in the system. In the future, we will consider how to choose the parameter (duration time) adaptation for different scenes. Fig1: Block Diagram of Video based crowd density estimation Fig2: Diagram for optical flow