Video Synopsis Based on Attention Mechanism and Local Transparent Processing

The increased number of video cameras makes an explosive growth in the amount of captured video, especially the increase of millions of surveillance cameras that operate 24 hours a day. Since video browsing and retrieval is time consuming, while video synopsis is one of the most effective ways for b...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.92603-92614
Main Authors: Chen, Shengbo, Liu, Xianrui, Huang, Yiyong, Zhou, Congcong, Miao, Huaikou
Format: Article
Language:English
Subjects:
Algorithms
attention mechanism
Browsing
Cameras
Compression ratio
deep learning
Detectors
Feature extraction
Moving object recognition
Object detection
Optimization methods
Task analysis
Trajectory
Trajectory optimization
transparency processing
Video compression
Video synopsis
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Summary:The increased number of video cameras makes an explosive growth in the amount of captured video, especially the increase of millions of surveillance cameras that operate 24 hours a day. Since video browsing and retrieval is time consuming, while video synopsis is one of the most effective ways for browsing and indexing such video that enables the review of hours of video in just minutes. How to generate the video synopsis and preserve the essential activities in the original video is still a costly and labor-intensive and time-intensive work. This paper proposes an approach to generating video synopsis with complete foreground and clearer trajectory of moving objects. Firstly, the one-stage CNN-based object detecting has been employed in object extraction and classification. Then, combining integrating the attention-RetinaNet with Local Transparency-Handling Collision (LTHC) algorithm is given out which results in the trajectory combination optimization and makes the trajectory of the moving object more clearly. Finally, the experiments show that the useful video information is fully retained in the result video, the detection accuracy is improved by 4.87% and the compression ratio reaches 4.94, but the reduction of detection time is not obvious.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2994613