Video Frame Skipping for Energy Reduction in Real-Time Vision
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Solution Overview
Problem
Current video processing systems face high energy consumption and bandwidth requirements due to the need to analyze and transmit a large number of video frames in real-time, especially in applications like autonomous vehicles and augmented reality, where computationally expensive vision models are used for object detection and analysis.
Innovation Solution
An apparatus and method that determine the number of frames to skip over based on object type and position using a pre-trained policy model trained with reinforcement learning, reducing the number of frames provided to the vision model for analysis while maintaining accuracy by employing a reward function that penalizes skipping over key frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all video frames are analyzed by vision models in real-time, then detection accuracy and reliability are maintained, but energy consumption and computational load increase significantly
Solution Approach 1:
The system skips processing certain video frames when object position and type remain unchanged across frames. A frame is skipped if the object detected in the current frame matches the object in the previous frame in terms of type and position, thereby reducing energy consumption while maintaining detection reliability.
Solution Approach 2:
The system performs preliminary comparison of object type and position between consecutive frames before deciding whether to process a frame with the vision model. This preliminary action determines if a frame is worth processing, avoiding unnecessary computational energy expenditure.
2Loss of information
If all video frames are transmitted for analysis, then complete information is available for computation, but bandwidth requirements and data transmission load increase
Solution Approach 1:
The system extracts only the essential information (object type and position) from video frames and uses this extracted information to determine whether to transmit the full frame for analysis. This extraction approach reduces bandwidth usage while ensuring that critical information is not lost.
Solution Approach 2:
The system introduces an intermediary comparison mechanism that sits between frame capture and frame transmission/processing. This intermediary evaluates whether the frame contains meaningful changes and only passes relevant frames forward, reducing the quantity of data transmitted while preserving information completeness.
3Use of energy by moving object
If frame skipping is increased to reduce processing load, then energy consumption decreases, but risk of missing key frames increases
Solution Approach 1:
The system uses feedback from the object detection results to control the frame skipping behavior. If object type or position changes are detected, the system adjusts its frame processing strategy to ensure key frames are not missed, while still maintaining energy efficiency during static periods.
4Measurement precision
If every frame is processed by the vision model, then processing precision is maintained, but productivity and processing speed decrease
Solution Approach 1:
The system skips vision model processing for frames where object type and position remain unchanged, thereby increasing processing throughput while maintaining analysis accuracy for frames that contain meaningful changes.
Solution Approach 2:
The system applies partial processing by using a lightweight comparison mechanism for all frames but reserving full vision model processing only for frames that require it based on object changes, optimizing the balance between processing precision and productivity.
Data Source
AI summary
The method includes receiving data representing a first frame of video content including a plurality of frames and determining, for at least one object in a first frame, an object type and position in the first frame. The method further includes determining a number of frames N to skip over based on the type and position of the object in the first frame and the type and position of one or more objects in one or more prior frames, and for providing the N+1 frame, and not the skipped-over frames.