Detection and classification of moving objects for an automated surveillance system
Automated surveillance system has been the subject of much research recently. A completely automated system means a computer will perforin the entire task from low level detection to higher level motion analysis. Since conventional system practically using human power to monitor and did not appli...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2006
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/3179/1/MOHD%20RAZALI%20BIN%20MD%20TOMARI%20-%2024p.pdf http://eprints.uthm.edu.my/3179/ |
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| Summary: | Automated surveillance system has been the subject of much research recently. A
completely automated system means a computer will perforin the entire task from low
level detection to higher level motion analysis. Since conventional system practically
using human power to monitor and did not applicable for a long hour monitoring, thus
automated system had been created to replace the conventional system. This thesis
focuses on a method to detect and classify a moving object that pass through the
surveillance area boundary. Moving object is detected by using combination of two
frame differencing and adaptive image averaging with selectivity. Technically, this
method estimate the motion area before updates the background by taking a weighted
average of non-motion area of the current background altogether with non-motion area
of the current frame of the video sequence. This step had created a focus of attention for
higher level processing and it helps to decrease computation time considerably. The
output of a motion-based detector is essentially a collection of foreground that might
correspond to the moving objects. But usually the output image produced from this process contaminated with noise and shadow. As a solution, morphological operation
has been employed as an approach to remove noise from the foreground object. Mutual
shadow that exists with the object had been abolished by combining chromatic colour
values with lightness variable. Then, standardized moment invariant is employed to
extract the features for each moving blobs. To recognize these blobs, the calculated
moment values are fed to a support vector machine module that is equipped with trained
extracted moment values for human and vehicle silhouettes. The system operates on
colour video imagery from a stationary camera. It can handle object detection in outdoor
environments and under changing illumination conditions. The applied post processing
module capable to remove noise and shadow from the detected objects with less than 1%
of error. Finally, classification algorithm that makes use of the extracted moment values
from the detected objects successfully categorize objects into pre-defined classes of
human and vehicle with 89.08% of accuracy. All the methods have been tested on video
data and the experimental results have demonstrated a fast and robust system |
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