Activity-Based Video Frame Ranking for VLM Input Selection
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Solution Overview
Problem
Existing video language models (VLMs) require specialized hardware and fail to capture all relevant information due to impractical computational and memory demands when processing every frame, leading to inefficient frame selection methods that miss important content.
Innovation Solution
Implement a ranking and management system for video frames based on content and metadata, using machine-learning models to assign rankings to frames, prioritizing those relevant to the processing objective, and selecting frames for further processing by VLMs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If every frame of the video is processed by the video language model, then the model captures all relevant information, but the computational and memory demands become prohibitively high
Solution Approach 1:
The patent segments the video stream into individual frames and applies ranking mechanisms to select only the most relevant frames for processing. This segmentation allows the system to handle video data in manageable portions rather than processing every frame, thereby reducing computational and memory demands while preserving important information.
Solution Approach 2:
The patent applies local quality by assigning different importance weights to different frames based on their content characteristics. Frames containing significant events, motion, or information are assigned higher quality weights and prioritized for processing, while less important frames are discarded. This ensures that computational resources are focused on the most informative portions of the video.
2Use of energy by moving object
If frames are sampled at predetermined time intervals to reduce data volume, then computational demands are reduced, but frames containing important information may be omitted
Solution Approach 1:
The patent implements dynamic frame selection by using ranking models that continuously evaluate frames based on their content characteristics such as motion, scene changes, and information density. This dynamic approach adapts to the actual content of the video stream, selecting frames based on their relevance rather than fixed time intervals, thereby preventing omission of important information while maintaining computational efficiency.
Solution Approach 2:
The patent employs feedback mechanisms where the ranking model uses video parameters and metadata from previously processed frames to inform the selection of subsequent frames. This feedback loop ensures that the system learns from the content patterns in the video and adjusts frame selection to capture important events, avoiding the blind spots inherent in predetermined sampling intervals.
3Productivity
If a ranking system is implemented to prioritize relevant frames, then processing efficiency is improved, but the complexity of the frame selection system increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing video parameters and metadata for each frame before the ranking process. This preliminary preparation includes extracting motion vectors, scene change indicators, and other relevant features in advance, so that the actual frame selection process can proceed efficiently without performing complex computations in real-time, thus balancing productivity improvement with acceptable system complexity.
Data Source
AI summary
Various examples, systems, and methods are disclosed relating to frame selection via activity-based ranking and optimization. A first computing system can receive a plurality of frames and metadata from a capture device capturing a video stream. The first computing system can generate, using a ranking model, a plurality of rankings for the plurality of frames based on a plurality of video parameters of the plurality of frames and the metadata, wherein the plurality of rankings correspond to a summarization of the video stream. The first computing system can determine at least one of the plurality of frames to provide to at least one buffer based on the plurality of rankings, wherein the at least one buffer stores a subset of frames of the plurality of frames. The first computing system can provide, from the at least one buffer, the subset of frames as input to a machine-learning model.