Behavioral profiling for adaptive video summarization: From generalization to personalization

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.