01-06-2012, 11:53 AM
Background Subtraction algorithm for Theft Detection
Background Subtraction algorithm for Theft Detection.pdf (Size: 78.88 KB / Downloads: 86)
Introduction
“Alarming Spy” is an application designed for
Monitoring and controlling the client remotely and
notifying the server whenever any foreign object enters to
the specific place that is being monitored or Motion is
detected. Further we can control the application either
using voice command or through the GUI to rotation of
stepper motor which in turn leads to move the Web Cam
to the appropriate direction given by the user.
The paper make use of Gaussian Distribution
algorithm which deals with Background Subtraction
which involves detection of any new object i.e.(Foreign
Object) that comes inside the vicinity monitored through
Web Cam. So that on detection of foreign object the client
will notify the remote server by alarming which intern
connected in LAN. So the User on the Server side can
perform the below operation.
· Capturing live video
· Control the direction of Web Cam
· Capture the snapshot
· Identify the detected foreign object
2. Historical Background
Background subtraction is the process of
comparing a background image with an observed image in
order to segment out the foreground objects. The
problematic domain has inspired a wealth of research,
developing a wide range of algorithms.
In this paper we implement a novel algorithm for
detecting moving objects from a static background scene
that contains shading and shadows using color images.
Although the background subtraction technique has been
used for years in many vision systems as a preprocessing
step for object detection and tracking, most of these
algorithms are susceptible to both global and local
illumination changes such as shadows and highlights.
These cause the consequent processes, e.g. tracking,
We
recognition, etc., to fail. This problem is the underlying
motivation of our work.
Conclusion
In this paper, we presented a novel background
subtraction algorithm for detecting moving objects from a
static background scene that contains shading and
shadows using color images.
The pixel based models are updated based on the
decisions made at the higher levels. We have compared
our results with the widely used Mixture of Gaussian
Background subtraction method. The algorithm is
especially suitable for detection of small, slow moving
objects. Combination of detection and tracking provides
the capability to handle small object localization.