A Focus on the Deep Learning-based Intelligent Video Surveillance System
Weigang Zhang, Youzi Li
Journal of Digital Information Management, Vol. 22, No. 4, 2024.
DOI: https://10.6025/jdim/2024/22/4/130-136
Link: https://www.dline.info/fpaper/jdim/v22i4/jdimv22i4_3.pdf
Abstract:
This paper focuses on applying a deep learning-
based intelligent video surveillance system, particularly
emphasising using the YOLOv7 model for object
detection. By reviewing the development of intelligent video
surveillance technology, we recognize the importance of
deep learning in computer vision. The structure and characteristics
of the YOLOv7 model are detailed, including
the input layer, backbone network layer, feature fusion
layer, and output layer. To validate the model's performance,
we conducted experiments on the VOC dataset,
and the results show that the YOLOv7 model achieved an
average detection accuracy of 0.89 on this dataset. The
experimental results demonstrate the efficiency, accuracy,
and robustness of the YOLOv7 model in intelligent video
surveillance, providing essential references for optimizing
and applying intelligent video surveillance systems.