Silhouette Based Human Motion Detection and Analysis for Real-Time Automated Video Surveillance

Murat EKİNCİ; Eyüp GEDİKLİ

Article Details

Silhouette Based Human Motion Detection and Analysis for Real-Time Automated Video Surveillance

Authors:

Murat EKİNCİ | Eyüp GEDİKLİ

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2005 Vol.13 Issue 2, pp.199-229

Document Type:

Article

Publication Date:

1/2005

Document Language:

Turkish

Document ID (Org No.):

2000744208

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632

Abstract:

 

In this paper, a real-time background modeling and  maintenance based human motion detection and analysis in an indoor and  an outdoor environments for visual surveillance system is described.   The system operates on monocular gray scale video imagery from a  static CCD camera.  In order to detect foreground objects, first,  background scene model is statistically learned using the redundancy  of the pixel intensities in a training stage, even the background is  not completely stationary.  This redundancy information of the each  pixel is separately stored in an history map shows how the pixel  intensity values changes till now.  Then the highest ratio of the  redundancy on the pixel intensity values in the history map in the  training sequence is determined to have initial background model of  the scene.  A background maintenance model is also proposed for  preventing some kind of falsies, such as, illumination changes (the  sun being blocked by clouds causing changes in brightness), or  physical changes (person detection while he is getting out or passing  in front of the parked car).  At the background modeling and  maintenance, the reliability and computational costs of the algorithm  presented are comparatively discussed with several algorithms.  Based  on the background modeling, candidate foreground regions are detected  using thresholding, noise cleaning and their boundaries extracted  using morphological filters.  Then for people detection, object  detection and classification approach for distinguishing a person, a  group of person from detected foreground objects (e.g., cars) using  silhouette shape and periodic motion cues is performed.  Finally, the  trajectory of the people in motion and several motion parameters  produced from the cyclic motion of silhouette of the object under  tracking are implemented for analyzing people activities such as  walking and running, in the video sequences.  Experimental results on  the different test image sequences demonstrate that the proposed  algorithm has an encouraging real-time background modeling based human  motion detection and analysis performance with relatively robust and  low computational cost.

 

In this paper, a real-time background modeling and  maintenance based human motion detection and analysis in an indoor and  an outdoor environments for visual surveillance system is described.   The system operates on monocular gray scale video imagery from a  static CCD camera.  In order to detect foreground objects, first,  background scene model is statistically learned using the redundancy  of the pixel intensities in a training stage, even the background is  not completely stationary.  This redundancy information of the each  pixel is separately stored in an history map shows how the pixel  intensity values changes till now.  Then the highest ratio of the  redundancy on the pixel intensity values in the history map in the  training sequence is determined to have initial background model of  the scene.  A background maintenance model is also proposed for  preventing some kind of falsies, such as, illumination changes (the  sun being blocked by clouds causing changes in brightness), or  physical changes (person detection while he is getting out or passing  in front of the parked car).  At the background modeling and  maintenance, the reliability and computational costs of the algorithm  presented are comparatively discussed with several algorithms.  Based  on the background modeling, candidate foreground regions are detected  using thresholding, noise cleaning and their boundaries extracted  using morphological filters.  Then for people detection, object  detection and classification approach for distinguishing a person, a  group of person from detected foreground objects (e.g., cars) using  silhouette shape and periodic motion cues is performed.  Finally, the  trajectory of the people in motion and several motion parameters  produced from the cyclic motion of silhouette of the object under  tracking are implemented for analyzing people activities such as  walking and running, in the video sequences.  Experimental results on  the different test image sequences demonstrate that the proposed  algorithm has an encouraging real-time background modeling based human  motion detection and analysis performance with relatively robust and  low computational cost.

Article Details

Silhouette Based Human Motion Detection and Analysis for Real-Time Automated Video Surveillance

Authors: Murat EKİNCİ | Eyüp GEDİKLİ

Publisher: TUBITAK

Journal: Turkish Journal of Electrical Engineering and Computer Science 2005 Vol.13 Issue 2, pp.199-229

Document Type: Article

Publication Date: 1/2005

Document Language: Turkish

Document ID (Org No.): 2000744208

Frequency: Bimonthly

eISSN: 1303-6203

pISSN: 1300-0632

Issues

Authors:

Murat EKİNCİ | Eyüp GEDİKLİ

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2005 Vol.13 Issue 2, pp.199-229

Document Type:

Article

Publication Date:

1/2005

Document Language:

Turkish

Document ID (Org No.):

2000744208

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632