Online Video Highlighting via Sparse Coding Dictionary Updates
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Solution Overview
Problem
Consumer-generated and surveillance videos lack structured content, making existing video summarization methods ineffective due to their unstructured nature and absence of features like scene boundaries and audio information, limiting the ability to efficiently extract important information.
Innovation Solution
An online video highlighting method that scans through video streams, constructs a dictionary to represent observed content, and updates it by incorporating video segments with high reconstruction errors, allowing for the generation of a short video highlight that captures the most important and interesting content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video summarization methods are used on edited videos with structured content, then summarization accuracy is improved, but these methods fail to work effectively on unstructured consumer-generated and surveillance videos
Solution Approach 1:
The patent develops a video summarization system that universally handles both structured edited videos and unstructured consumer-generated/surveillance videos through a unified approach. The system uses unsupervised learning with sparse coding and dictionary learning that does not rely on structured features like scene boundaries or audio, making it adaptable to multiple video types while maintaining summarization accuracy
Solution Approach 2:
The patent transforms the video summarization problem from relying on structured parameters (scene boundaries, audio cues) to using unsupervised learning parameters (sparse coding coefficients, dictionary atoms). This parameter transformation allows the system to extract meaningful summaries from unstructured videos by learning relevant features directly from the video data without predefined structures
2Loss of information
If hours of video data are processed to create comprehensive summaries, then information completeness is improved, but processing time becomes prohibitively long
Solution Approach 1:
The patent extracts only the most salient and informative portions of video data by using sparse coding to identify and select key video segments. Instead of processing or summarizing all video content, the system extracts representative frames and segments that capture the essential information, significantly reducing processing time while maintaining information completeness
Solution Approach 2:
The patent applies partial action by processing only a subset of video frames and segments that are deemed most important. The sparse coding framework allows the system to focus computational resources on salient portions of the video rather than uniformly processing all content, achieving efficient summarization without sacrificing critical information
3Productivity
If key frames are selected from original video to create summaries, then processing speed is improved, but the summaries lack temporal continuity and fail to help viewers understand the original video
Solution Approach 1:
The patent segments the video into meaningful units and uses sparse coding to select representative segments for the summary. Rather than selecting isolated key frames, the system identifies and extracts contiguous video segments that maintain temporal relationships, ensuring the summary preserves the chronological flow and contextual understanding of the original video
Solution Approach 2:
The patent introduces sparse coding coefficients and dictionary atoms as intermediaries that capture temporal relationships between video segments. These mathematical constructs serve as mediators that preserve temporal continuity information, allowing the system to select segments that maintain chronological coherence and help viewers understand the sequence of events in the original video
Data Source
AI summary
With the widespread availability of video cameras, we are facing an ever-growing enormous collection of unedited and unstructured video data. Due to lack of an automatic way to generate highlights from this large collection of video streams, these videos can be tedious and time consuming to index or search. The present invention is a novel method of online video highlighting, a principled way of generating a short video highlight summarizing the most important and interesting contents of a potentially very long video, which is costly both time-wise and financially for manual processing. Specifically, the method learns a dictionary from given video using group sparse coding, and updates atoms in the dictionary on-the-fly. A highlight of the given video is then generated by combining segments that cannot be sparsely reconstructed using the learned dictionary. The online fashion of the method enables it to process arbitrarily long videos and starts generating highlights before seeing the end of the video, both attractive characteristics for practical applications.