Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Suhandas, a; * | G, Santhosh Kumarb
Affiliations: [a] Department of Electronics and Communication Engineering, A.J. Institute of Engineering and Technology, Kottara Chowki, Mangaluru, India | [b] Department of Electronics and Communication Engineering, East West College of Engineering, Yelahanka New Town, Bengaluru, India
Correspondence: [*] Corresponding author: Suhandas, Department of Electronics and Communication Engineering, A.J. Institute of Engineering and Technology, Kottara Chowki, Mangaluru 575006, India. E-mail: suhandas099@gmail.com.
Abstract: Video condensation or synopsis is an effective solution for problems regarding video storage and video browsing. The proposed model contributed to developing the video condensation framework for efficient video browsing and video retrieval. In the first stage, the videos are gathered from the surveillance videos. Here, the frames are generated, and then the video backgrounds are extracted. The objects from the frames are acquired through the support of Yolov3. Next, the optimal stitching is done based on the time and object activity of video frames using the Improved Blue Monkey Optimization (IBMO) algorithm. Moreover, video condensation is performed to get the compact video for making better browsing and retrieval of video. The video browsing and retrieval are performed under two phases such as training and testing phases and both phases are done by gathering the videos and followed by the feature extraction using VGG16, where the heuristic improvement is made by the same IBMO algorithm. Then, the extracted deep features from video segments are clustered based on Fuzzy C-means (FCM) clustering for combining the extracted features. These features are stored in the feature database in the training phase. Next, in the testing phase, video browsing and retrieval are performed by considering the queries gathered from the standard dataset. The features of query videos are extracted, which are compared based on Multi-Similarity Function (MSF) with the features in the database for retrieving the video segments. Experimental results show that the developed IBMO-VGG-MSF-based video condensation saves computational loads compared to the previous methods without compromising the condensation ratio and visual quality.
Keywords: Video condensation, video browsing and retrieval, surveillance videos, improved blue monkey optimization, multi-similarity function, optimal stitching, VGG16, fuzzy C-means clustering
DOI: 10.3233/IDT-220303
Journal: Intelligent Decision Technologies, vol. 17, no. 3, pp. 687-712, 2023
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl