Entity-Based Video Temporal Segmentation
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Solution Overview
Problem
Existing video processing methods rely on shot-based temporal segmentation, which fails to differentiate between semantically distinct scenes in videos with smooth transitions, lacking effective linkage between low-level video data and high-level semantic fields.
Innovation Solution
The method involves decoding videos into frames, annotating entities with timestamps and confidence scores, smoothing the data to reduce noise, and detecting segment boundaries to generate overall temporal segmentation that reflects the video's semantics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If shot-based temporal segmentation is used, then video streams can be segmented into visually distinct segments, but semantically distinct scenes with smooth transitions cannot be differentiated
Solution Approach 1:
The patent changes the segmentation parameter from visual shot boundaries to entity presence/absence over time. By tracking entity timestamps and confidence scores across video frames, the system identifies semantic scene boundaries based on when entities appear or disappear, rather than relying on visual shot transitions. This parameter change enables differentiation of semantically distinct scenes with smooth transitions.
Solution Approach 2:
The patent introduces entity detection results as an intermediary between raw video data and semantic scene segmentation. Entity timestamps and confidence scores serve as intermediate representations that bridge low-level video frames and high-level semantic understanding, enabling accurate scene differentiation through entity-based temporal patterns.
2Loss of information
If entity-based temporal segmentation is implemented, then semantic scene differentiation is improved, but processing complexity and computational requirements increase
Solution Approach 1:
The patent performs entity detection and timestamp generation on sampled video frames before conducting temporal segmentation. By pre-processing the video to identify and annotate entities with timestamps and confidence scores in advance, the system reduces the complexity of the actual segmentation process while preserving semantic information.
Solution Approach 2:
The patent applies smoothing to only the most significant entity time series data rather than processing all video frames uniformly. By selectively smoothing entity presence data and focusing computational resources on key entities with high confidence scores, the system achieves effective semantic segmentation while reducing overall processing complexity.
Data Source
AI summary
A solution is provided for temporally segmenting a video based on analysis of entities identified in the video frames of the video. The video is decoded into multiple video frames and multiple video frames are selected for annotation. The annotation process identifies entities present in a sample video frame and each identified entity has a timestamp and confidence score indicating the likelihood that the entity is accurately identified. For each identified entity, a time series comprising of timestamps and corresponding confidence scores is generated and smoothed to reduce annotation noise. One or more segments containing an entity over the length of the video are obtained by detecting boundaries of the segments in the time series of the entity. From the individual temporal segmentation for each identified entity in the video, an overall temporal segmentation for the video is generated, where the overall temporal segmentation reflects the semantics of the video.