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A video summarization framework based on activity attention modeling using deep features for smart campus

Wasim Muhammad1, Imran Ahmed1, Jamil Ahmad1,2

  • 1Center of Excellence in Information Technology, Institute of Management Sciences (IMSciences), Peshawar, Peshawar, KPK, Pakistan.

Peerj. Computer Science
|May 2, 2022
PubMed
Summary

This study introduces a keyframe extraction method for summarizing academic activities. The approach uses deep learning to identify essential campus events, creating concise video representations for faster retrieval.

Keywords:
Dats scienceDeep learningEmerging technologiesMachine learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Video Processing

Background:

  • Digital monitoring and surveillance are prevalent in academic institutions, generating vast amounts of video data.
  • Searching and retrieving specific content from large video repositories is time-consuming and inefficient.
  • Effective video summarization techniques are crucial for efficient navigation and content access.

Purpose of the Study:

  • To develop an effective keyframe extraction method for summarizing academic activities.
  • To create a concise video representation that preserves essential information from original videos.
  • To improve the speed and efficiency of video content retrieval in academic settings.

Main Methods:

  • Fine-grain activity recognition using a deep Convolutional Neural Network (CNN) model on the Campus Activities Dataset (CAD).
  • Modeling activity attention scores to identify significant events within the video data.
  • Utilizing attention scores to extract salient video frames and applying an inter-frame similarity index to eliminate redundancy.

Main Results:

  • The proposed keyframe extraction method successfully summarizes academic activities.
  • The framework generates representative keyframes, preserving essential information while reducing video length.
  • Experimental results demonstrate the effectiveness of the summarization process on various test videos.

Conclusions:

  • The developed keyframe extraction technique offers an efficient solution for summarizing campus surveillance videos.
  • This method significantly aids in fast navigation and retrieval of critical information from extensive video archives.
  • The approach holds promise for enhancing video content management in academic institutions.