Motion Detection Using Pixel Thresholding and Camera Control
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Solution Overview
Problem
Existing motion detection systems face challenges in accurately distinguishing between genuine motion and noise or false positives, particularly due to camera settings like auto-white balance and auto-focus, which can lead to incorrect detection of motion.
Innovation Solution
An image processor analyzes video frames by classifying pixel locations as static or dynamic based on frame threshold changes, generates a bounding box around motion pixel locations, and adjusts camera settings such as disabling auto-white balance and auto-focus to reduce noise susceptibility, thereby enhancing motion detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If auto-white balance and auto-focus are enabled, then the camera can adapt to changing lighting conditions and maintain focus, but false motion detection increases due to automatic adjustments being misinterpreted as motion
Solution Approach 1:
The patent extracts and removes the source of false motion detection by disabling auto-white balance and auto-focus features during motion detection operations. This eliminates the automatic adjustments that were being misinterpreted as motion, thereby resolving the contradiction between adaptability and detection reliability.
Solution Approach 2:
The patent applies preliminary action by configuring camera settings before motion detection begins. Specifically, it disables auto-white balance and auto-focus in advance to prevent them from causing false motion detection during the detection process, while still allowing the camera to capture images.
2Measurement precision
If motion detection sensitivity is increased to detect subtle movements, then more genuine motion events are detected, but false positives from noise increase
Solution Approach 1:
The patent applies local quality by making motion detection pixels uniform in color rather than allowing individual pixel variations. This reduces noise interference and false positives while maintaining the ability to detect genuine motion, effectively resolving the contradiction between sensitivity and noise resistance.
Solution Approach 2:
The patent enforces homogeneity by requiring motion detection pixels to have uniform color values. This eliminates pixel-level variations that could be misinterpreted as motion, thereby reducing false positives while preserving genuine motion detection capability.
3Measurement precision
If detailed pixel-level analysis is performed to improve motion detection accuracy, then genuine motion can be better distinguished, but processing complexity and computational load increase
Solution Approach 1:
The patent changes key parameters to simplify processing while maintaining accuracy. It modifies pixel color values to be uniform, disables automatic camera adjustments, and uses simplified motion detection logic that compares pixel positions across frames. These parameter changes reduce computational complexity while preserving genuine motion detection capability.
Data Source
AI summary
Various examples are directed to systems and methods for detection motion in video data. An image processor may receive video data. A first frame and a second frame may describe a plurality of pixel locations. The second frame may be positioned after the first frame in the frame sequence. The image processor may determine that a first subset of pixel locations from the plurality of pixel locations differ by less than a threshold amount between frames for at least a threshold number of frames in the frame sequence prior to the first frame. The image processor may identify from the first subset of pixel locations, a second subset of pixel locations that differ by more than the threshold amount between the first frame and the second frame and determine that a number of pixel locations in the second subset of pixel locations is greater than a motion pixel threshold.