Video Playback System Using Emotion-Labeled Segments
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Solution Overview
Problem
Users often experience poor video watching experiences due to selecting videos they do not like, as they lack knowledge of the content beforehand, leading to segments they dislike being played.
Innovation Solution
A method and system that divide videos into segments labeled with emotion categories based on user preferences, using neural networks to determine user emotions from physical parameters like facial expressions and physiological data, and correspond these to video content, allowing selective playback of segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the user manually selects video content, then the user has control over what to watch, but the user may watch segments they dislike due to lack of knowledge about the content
Solution Approach 1:
The system continuously monitors user physiological parameters (heart rate, skin conductance, facial expressions) during video playback and uses this feedback to dynamically adjust video segment playback. This real-time feedback loop enables the system to understand user emotional states and automatically skip segments that would not be enjoyed, resolving the contradiction between manual control and experience quality.
Solution Approach 2:
The system enables videos to play themselves by automatically selecting and playing only the segments that match the user's current emotional state and preferences. The video content serves itself by using embedded emotion labels and the user's physiological data to make autonomous playback decisions, eliminating the need for manual user selection while ensuring high-quality viewing experience.
2Adaptability or versatility
If the system divides video into segments and uses neural networks to analyze user emotions, then the video playback is tailored to user preferences, but the system complexity increases
Solution Approach 1:
The video is divided into multiple segments with different emotion labels (happy, sad, exciting, etc.), and the system only plays segments that match the user's current emotional state. This segmentation approach enables personalized playback without requiring complex real-time analysis of the entire video, as the pre-labeled segments can be directly matched with user emotions.
Solution Approach 2:
The emotion labels for video segments are pre-generated using neural networks before playback. This preliminary action allows the system to avoid complex real-time emotion analysis during video playback, as the matching between video segments and user emotions can be done through simple label comparison rather than complex computational analysis.
Data Source
AI summary
A video playing method, a video playing device, a video playing system, an apparatus, and a computer-readable storage medium are provided. The video playing method includes dividing a video to be played into a plurality of video segments and labelling, respectively, the plurality of video segments with emotion categories of a user as labels according to a pre-stored correspondence between the emotion categories of the user and video contents, and determining whether each video segment is to be played according to the label of the video segment.