Video Summarization Lecture
Nowadays, digital videos are everywhere and revolutionize very many domains, notably:
-Digital Media (video/movie) Content Production and Broadcasting,
-Social Media Streaming and Analytics (g., YouTube),
-Mobile computing and streaming
-Videoconferencing
-Medical/Biological/Dental Imaging and Diagnosis,
-Big Visual Data Analytics,
-Internet and Communications (media broadcasting, streaming).
-Scientific Imaging of any sort, e.g., Physics.
Furthermore, Video Processing and Analysis enables diverse applications, in unison with Computer Vision and Machine Learning:
-Autonomous Systems (cars, drones, vessels) Perception,
-Robotics Perception and Control,
-Intelligent Human-Machine Interaction,
-Anthropocentric (human-centered) Computing,
-Smart Cities/Buildings and Assisted living.
Visual Computing, encompassing Computer Vision and Video Processing and Analysis, coupled with AI (notably Machine Learning and Deep Neural Network) advances hit the news almost every day.
This lecture overviews Video Summarization that has many applications in video description, search, retrieval and browsing. It covers the following topics in detail: Video Summarization Models: a) Static video summary (storyboard), Video Captioning, Key framing extraction, b) Dynamic video summarization (skimming). Video Summarization Techniques (Video Summarization with Global and Local Features, Scene identification with global features, Keyframe selection with local features. Event-based video summarization. Motion and Color Based video Summarization. Object-Based video summarization. Attention-based video summarization. Clustering-based Video summarization. Selection of shot / shot boundaries-based video summarization. Trajectory-based Video Summarization). Supervised learning for Video Summarization. Unsupervised learning for Video Summarization. Video Summarization with Neural Networks (NN). Video Summarization Applications.