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Behavioral profiling for adaptive video summarization: From generalization to personalization.

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  • 1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University) (SIU), Lavale, Pune, Maharashtra, India.

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Summary
This summary is machine-generated.

This study introduces a two-phase method for summarizing videos using machine learning and user engagement. It addresses challenges in managing CCTV footage by enhancing accessibility and navigation through personalized content curation.

Keywords:
Deep Learning, Computer VisionDeep Sort AlgorithmInformation RetrievalKeyframe Extraction Based Single View Query Dependent Video SummarizationQuery based SummarizationVideo SummarizationYOLO V5

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Multimedia Systems

Background:

  • Managing large volumes of multimedia content, particularly CCTV footage, presents significant challenges in storage, accessibility, and efficient navigation.
  • Existing video summarization techniques often lack personalization and struggle with the sheer scale of digital video data.

Purpose of the Study:

  • To develop an encompassing technique for summarizing videos that merges machine learning with user engagement.
  • To address the challenges of storage, accessibility, and navigation in managing multimedia content, specifically CCTV footage.
  • To enhance user interaction and personalize content curation for video summaries.

Main Methods:

  • Phase I: Video summarization based on keyframe detection and behavioral analysis using YOLOv5 for object recognition, Deep SORT for object tracking, and Single Shot Detector (SSD).
  • Phase II: User Interest Based Video summarization driven by machine learning, incorporating user preferences for personalized content curation.
  • Leveraging Natural Language Toolkit (NLTK), OpenCV, TensorFlow, and EfficientDET model for customized video summary generation.

Main Results:

  • A two-phase methodology that improves video summarization through objective analysis and user-centric personalization.
  • Enhanced ability to handle overwhelming amounts of video data on digital platforms.
  • Generation of customized video summaries tailored to individual user preferences.

Conclusions:

  • The proposed innovative approach effectively tackles the complex challenges of managing multimedia data.
  • Combining machine learning techniques with user engagement offers a significant advancement in video summarization.
  • The system enhances user interactions and provides efficient solutions for digital content management.