Salient Video Frame Selection Using Photo Context
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Solution Overview
Problem
Conventional methods for creating static collages from videos are either time-consuming for users due to the need to sift through numerous frames or result in randomly selected frames that may not adequately represent the event, often missing important and visually appealing frames.
Innovation Solution
An image processing module selectively filters video frames using extrinsic information from photos related to the video, such as temporal and spatial proximity, to identify and extract salient frames that are pertinent and visually appealing, thereby reducing the number of frames the user needs to review and ensuring important moments are represented.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all constituent frames of a video are presented to the user for selection, then the user can choose the most visually appealing frames, but the user must sift through hundreds of frames which is time-consuming and tedious
Solution Approach 1:
The system performs preliminary analysis of video frames using machine learning models to identify visually appealing frames before presenting them to the user. This pre-processing step filters out poor quality frames automatically, so the user only needs to review the pre-selected candidates rather than sifting through all hundreds of frames.
Solution Approach 2:
An automated frame selection algorithm acts as an intermediary between the raw video frames and the user. This intermediary uses computer vision techniques to evaluate frame quality metrics such as composition, lighting, and subject matter, thereby reducing the user's workload while maintaining selection quality.
2Duration of action of moving object
If the video duration is increased to capture more event moments, then more important moments are recorded, but the number of frames to review increases from 300 to potentially thousands
Solution Approach 1:
The system performs preliminary analysis of video frames using machine learning models to identify visually appealing frames before presenting them to the user. This pre-processing step filters out poor quality frames automatically, so the user only needs to review the pre-selected candidates rather than sifting through all hundreds of frames.
Solution Approach 2:
Instead of requiring the user to review all frames or even a large random subset, the system uses automated analysis to identify and present only the most promising frames. This partial review approach focuses user attention on a small subset of high-quality candidates that are most likely to be suitable for the collage.
3Loss of time
If random decimation of frames is performed to reduce the number of frames for user review, then the user reviews fewer frames, but the selected frames may be wholly inadequate to represent the event
Solution Approach 1:
An automated frame selection algorithm acts as an intermediary between the raw video frames and the user. This intermediary uses computer vision techniques to evaluate frame quality metrics such as composition, lighting, and subject matter, thereby reducing the user's workload while maintaining selection quality.
Solution Approach 2:
The system uses feedback from machine learning models that have been trained to recognize visually appealing and representative frames. These models provide quality assessments for each frame, allowing the system to select frames that are most likely to accurately represent the event while maintaining high visual quality standards.
4Measurement precision
If the user manually selects frames from hundreds of constituent frames, then the user can choose frames with good composition and visual appeal, but the process is tedious and impractical for videos of moderate duration
Solution Approach 1:
The system performs preliminary analysis of video frames using machine learning models to identify visually appealing frames before presenting them to the user. This pre-processing step filters out poor quality frames automatically, so the user only needs to review the pre-selected candidates rather than sifting through all hundreds of frames.
Solution Approach 2:
An automated frame selection algorithm acts as an intermediary between the raw video frames and the user. This intermediary uses computer vision techniques to evaluate frame quality metrics such as composition, lighting, and subject matter, thereby reducing the user's workload while maintaining selection quality.
Data Source
AI summary
Salient video frame establishment is described. In one or more example embodiments, salient frames of a video are established based on multiple photos. An image processing module is capable of analyzing both video frames and photos, both of which may include entities, such as faces or objects. Frames of a video are decoded and analyzed in terms of attributes of the video. Attributes include, for example, scene boundaries, facial expressions, brightness levels, and focus levels. From the video frames, the image processing module determines candidate frames based on the attributes. The image processing module analyzes multiple photos to ascertain multiple relevant entities based on the presence of entities in the multiple photos. Relevancy of an entity can depend, for instance, on a number of occurrences. The image processing module establishes multiple salient frames from the candidate frames based on the multiple relevant entities. Salient frames can be displayed.