Video Segment Generation Using Temporal Self-Similarity Matrix
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Solution Overview
Problem
Existing video processing systems struggle to efficiently identify and generate intelligent temporal video segments, as they lack effective features to capture temporal context and are not suitable for on-device processing, leading to complexity and inefficiency.
Innovation Solution
The proposed solution utilizes a Temporal self-similarity Matrix (TSSM) to establish relationships between video frames, allowing for the prediction of class-agnostic boundaries and the generation of intelligent temporal segments. This approach also considers user interactions and contexts to refine and rank the segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing methods like BSN or BMN are used to generate video segments, then video processing can be performed, but the systems are complex and not suitable for on-device processing
Solution Approach 1:
The patent segments the video processing task into distinct stages: shot boundary detection, subject change detection, and action change detection. Each stage operates independently with specialized features, reducing overall system complexity while enabling on-device processing. The segmentation allows modular implementation where each component can be optimized separately for resource-constrained environments.
Solution Approach 2:
The patent extracts and removes complex components from traditional video segmentation systems. Specifically, it eliminates the need for complex fully connected layers and 3D convolutions by using simplified feature extraction based on shot boundaries, subject changes, and action changes. This extraction of essential features while removing unnecessary complexity enables deployment on mobile devices.
2Measurement precision
If traditional video segmentation methods are used, then video clips can be generated, but they lack effective features to capture temporal context
Solution Approach 1:
The patent performs preliminary detection of shot boundaries, subject changes, and action changes before final segment generation. By pre-identifying these critical temporal markers, the system captures temporal context early in the processing pipeline, ensuring that subsequent segment generation is based on accurate temporal understanding rather than losing this information in later stages.
Solution Approach 2:
The patent introduces intermediate features as mediators between raw video data and final segments. These intermediates include shot boundary detections, subject change detections, and action change detections that serve as temporal context carriers. These intermediary representations preserve temporal information that would otherwise be lost, bridging the gap between raw pixels and meaningful video segments.
3Reliability
If multiple separate pipelines for test, audio and visual cues are used, then comprehensive analysis is achieved, but processing becomes more complex and time-consuming
Solution Approach 1:
The patent merges multiple analysis pipelines into a unified framework. Instead of separately processing test cues, audio cues, and visual cues through independent pipelines, the system integrates them into a single cohesive process that detects shot boundaries, subject changes, and action changes simultaneously. This merging maintains comprehensive analysis capability while eliminating the overhead of multiple separate processing streams, thereby improving processing speed.
Data Source
AI summary
A method for generating at least one segment of a video by an electronic device is provided. The method includes identifying at least one of a context associated with the video and an interaction of a user in connection with the video, analyzing at least one parameter in at least one frame of the video with reference to at least one of the context and the interaction of the user, wherein the at least one parameter includes at least one of a subject, an environment, an action of the subject, and an object, determining the at least one frame in which a change in the at least one parameter occurs, and generating at least one segment of the video comprising the at least one frame in which the parameter changed as a temporal boundary of the at least one segment.