Abstract
Video-surveillance and traffic analysis systems can be heavily improved using vision-based techniques to extract, manage and track objects in the scene. However, problems arise due to shadows. In particular, moving shadows can affect the correct localization, measurements and detection of moving objects. This work aims to present a technique for shadow detection and suppression used in a system for moving visual object detection and tracking. The major novelty of the shadow detection technique is the analysis carried out in the HSV color space to improve the accuracy in detecting shadows. This paper exploits comparison of shadow suppression using RGB and HSV color space in moving object detection and results in this paper are more encouraging using HSV colour space over RGB colour space.
Highlights
Surveillance systems have wide demand in public areas, such as airports, subways, entrance to buildings
The Gaussian mixture model (GMM) [1] represented the statistics of one pixel over time can cope with multi-modal background distributions
Gaussian Mixture Model (GMM) is thought to be one of the best background modeling methods and works well when gradual changes appear in the scene [2] [3]
Summary
Surveillance systems have wide demand in public areas, such as airports, subways, entrance to buildings. In this context, reliable detection of moving objects is most critical requirement for the surveillance systems. To detect a moving object, a surveillance system usually utilizes background subtraction. The Gaussian mixture model (GMM) [1] represented the statistics of one pixel over time can cope with multi-modal background distributions. Gaussian Mixture Model (GMM) is thought to be one of the best background modeling methods and works well when gradual changes appear in the scene [2] [3]. The GMM method models the intensity of each pixel with a mixture of k Gaussian distributions. Where k is the number of distributions (currently, 3 to 5 is used), ωi,t is the weight of the kth Gaussian in the mixture at www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 4, No.1, 2013 time t and η (Xt, μi,t , Σi,t) the Gaussian probability density function.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
