Video Frame Change Detection for Computer Vision Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computer vision technologies face inefficiencies in processing video data, leading to high system load and power consumption, especially when dealing with unchanging video frames or frames without target objects.
Innovation Solution
A method that acquires video frames, compares pixel values between adjacent frames to determine changes, and selectively processes only changed frames with target objects, using a Spiking Neural Network (SNN) model for efficient change detection and a convolutional neural network (CNN) for object recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all video frames are processed for computer vision tasks, then comprehensive object detection and scene analysis is achieved, but system load and power consumption increase significantly
Solution Approach 1:
The patent extracts only the necessary information (changed pixels) from each video frame by comparing adjacent frames. By identifying and processing only the changed regions rather than the entire frame, the system reduces computational load and power consumption while maintaining accurate object detection in relevant areas.
Solution Approach 2:
The patent segments the video processing task by dividing frames into changed and unchanged regions. Through pixel-level comparison between adjacent frames, the system identifies only the segments (changed pixels) that require further processing, separating useful computational work from redundant processing of static regions.
2Reliability
If all video frames are processed for computer vision tasks, then comprehensive object detection is achieved, but system load increases
Solution Approach 1:
The patent extracts only the necessary information (changed pixels) from each video frame by comparing adjacent frames. By identifying and processing only the changed regions rather than the entire frame, the system reduces computational load and power consumption while maintaining accurate object detection in relevant areas.
Solution Approach 2:
Instead of processing the entire video frame completely, the patent applies partial action by processing only the changed pixel regions. This selective processing approach reduces system load while maintaining sufficient accuracy for object detection tasks in the relevant changed areas.
3Measurement precision
If pixel value comparison is performed between adjacent frames, then changed frames are identified accurately, but processing time increases
Solution Approach 1:
Instead of processing the entire video frame completely, the patent applies partial action by processing only the changed pixel regions. This selective processing approach reduces system load while maintaining sufficient accuracy for object detection tasks in the relevant changed areas.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing a video. The method includes acquiring a video, where the video includes at least a current frame and a previous frame that are adjacent to each other. The method further includes determining, based on a first pixel value of a pixel in the current frame and a second pixel value of a corresponding pixel in the previous frame, whether the current frame has changed relative to the previous frame. The method further includes determining availability of the current frame for a computer vision task if it is determined that the current frame has changed relative to the previous frame. With the method, video data that needs to be processed is reduced, the task load of a computing device is lowered, system power consumption is improved, and data processing efficiency is improved.