Abstract:
I his project report proposes a way to delect default camera events through image
analysis, to ensure good image quality and the right platform for watching
smveillance videos. 1 he first method removes the reduced referenced features in
most legions in the surveillance image, and then detects confusing scenarios by
analysing the vaiiation ol features when the image quality decreases and the viewing
field changes. Recently, confusing camera detection has attracted growing interest to
produce real-time camera alerts for video surveillance systems.
Existing methods lor confusing camera still do not have enough power to
detect a wide variety of abnormalities, and they do not have the power to improve
themselves in the case of abortions by self-study. Therefore, this paper proposes
moiphological analysis and in-depth reading based on an unconventional camera
detection method to detect a wide variety of abnormalities. Morphological analysis
is used lor easy con I using camera detection to speed up processing speed, and in depth reading is used to detect complex camera distractions to improve accuracy.
1 est results show that the accuracy of the proposed acquisition method gains
than 95%.
more
The system starts with the previous process of video capture with limited
video, inverting and sliding. Sorting, sorting, resizing and extracting features
also done in this process. Next, a network feed process is requested to generate
output matrix. Based on the output matrix, the known character can be determined.
This program is designed to customize the network to each user. Recommendations
for future development and conclusions are also included in the report.
