In surveillance systems, vast amounts of data are collected from different sources to monitor ongoing video activities. Usually, video data is passively captured by visual sensors and forwarded to the command center, without intelligent Edge functionalities to select essential video information and locally detect abnormal events therein. These shortcomings often seen in practical surveillance scenarios lead to a wastage of storage resources and make data management, retrieval, and informed decision complex and time-consuming. Therefore, endowing visual sensors with video summarization capabilities is of utmost importance for smarter surveillance systems. This study departs from this rationale to propose an efficient neural networks-based video summarization method for surveillance systems. The proposed approach learns how to optimally segment a video by measuring informative features from the data flow, followed by memorability and entropy to maintain the relevance and diversity of the video summary produced on the Edge. Experimental results over benchmark datasets reveal that the proposed scheme outperforms other state-of-the-art counterparts and proves the effectiveness of our method for video summarization in smart cities.
Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
Subtitle of host publication4-7 December 2022
EditorsHisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, Keeley Crockett
Place of PublicationSingapore
PublisherIEEE Xplore
Number of pages6
ISBN (Electronic)978-1-6654-8768-9
ISBN (Print)978-1-6654-8769-6
Publication statusPublished - 30 Jan 2023

Publication series

NameProceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022


  • Artificial intelligence (AI)
  • Computer Vision
  • Deep Learning
  • Energy-Efficiency
  • IoT
  • Smart City
  • Surveillance Systems
  • Video Summarization
  • Visualization
  • Smart cities
  • Surveillance
  • Computational modeling
  • Benchmark testing
  • Streaming media
  • Feature extraction


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