Static Video Frame Classification for Motion-Triggered Detection
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Solution Overview
Problem
Existing security systems struggle to efficiently process video data in real-time to detect specific events or objects and provide timely notifications based on these detections.
Innovation Solution
A method and system for classifying objects and events using static video frames by comparing frames before and after a motion trigger, employing algorithms like background subtraction and machine learning models to identify changes and generate notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If video data is processed in real-time to detect events and objects, then detection speed and responsiveness improve, but computational complexity and processing time increase
Solution Approach 1:
The patent segments video processing by selecting only specific frames (pre-trigger and post-trigger) rather than processing the entire video stream. This divides the continuous processing task into discrete, manageable frame comparisons, reducing computational complexity while maintaining real-time detection capability.
Solution Approach 2:
The system performs preliminary actions by capturing and storing video frames before the trigger event occurs. This allows the processing system to have pre-prepared data ready for immediate comparison when an event is detected, reducing processing time and enabling faster real-time response without increasing ongoing computational complexity.
2Reliability
If continuous video monitoring is performed to ensure detection accuracy, then detection reliability improves, but energy consumption and processing load increase
Solution Approach 1:
Instead of continuous processing, the system employs periodic action by processing only specific frames at predetermined intervals (pre-trigger and post-trigger frames). This periodic approach maintains detection reliability by capturing relevant moments while significantly reducing energy consumption compared to continuous video analysis.
Solution Approach 2:
The patent extracts only the essential information needed for detection by selecting specific key frames rather than analyzing the entire video stream. This extraction approach maintains detection reliability by focusing on critical moments while reducing the processing load and energy consumption associated with analyzing all video data.
3Measurement precision
If multiple video frames are analyzed to improve object classification accuracy, then classification precision improves, but processing time and computational resources increase
Solution Approach 1:
The patent segments the video analysis task into comparison of only two specific frames (pre-trigger and post-trigger) rather than analyzing multiple consecutive frames. This segmentation maintains classification precision by focusing on the most relevant frames that show the event occurrence, while reducing processing time and computational resource requirements.
Data Source
AI summary
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for performing event or object classification. An example process can include receiving a first trigger corresponding to a first motion event within a field of view of a first image sensor; selecting a first video frame from a sequence of video frames captured by the first image sensor, wherein the first video frame is captured prior to the first trigger; selecting a second video frame from the sequence of video frames, wherein the second video frame is captured after the first trigger; determining at least one difference between the first video frame and the second video frame; determining, based on the at least one difference, at least one of an object classification and an event classification; and generating a notification that corresponds to the object classification or the event classification.