Video Object Segmentation Using PIR and 3D Sensor Fusion
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Solution Overview
Problem
Existing digital image processing systems face inefficiencies and unreliability in object segmentation, particularly when analyzing complex motion and depth in video sequences, leading to high computational resource consumption or low reliability.
Innovation Solution
A system that detects specific types of movement within a video sequence by using a combination of PIR sensors, infrared LEDs, and 3D sensors to selectively enable different operating modes, allowing for efficient object segmentation by focusing on human or non-human motion, thereby reducing computational load and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system performs object segmentation with a high degree of complexity (analyzing depth and regions of interest), then the reliability of object segmentation is improved, but the computational resource consumption increases and processing speed decreases
Solution Approach 1:
The system performs preliminary motion detection using PIR sensors and 3D sensors before conducting detailed object segmentation. This preliminary action identifies regions containing motion, allowing the system to focus subsequent complex segmentation operations only on those specific regions rather than processing the entire image, thus maintaining reliability while improving processing speed
Solution Approach 2:
The system divides the image processing task into multiple segments: first detecting motion regions using sensors, then performing object segmentation only on those detected regions. This segmented approach reduces the overall computational load while maintaining segmentation reliability in the regions that require it
2Productivity
If the system performs object segmentation with a low degree of complexity, then the computational resource consumption decreases and processing speed increases, but the reliability of object segmentation deteriorates
Solution Approach 1:
The system applies different processing qualities to different regions of the image: simple motion detection is applied to the entire image, while complex object segmentation is applied only to specific regions where motion is detected. This local quality approach ensures high reliability where needed while maintaining overall processing efficiency
3Measurement precision
If the system uses multiple sensors (PIR sensors, infrared LEDs, 3D sensors) for motion detection, then the accuracy of motion classification is improved, but the device complexity increases
Solution Approach 1:
The system combines multiple sensor types (PIR sensors for thermal motion detection, infrared LEDs for illumination, and 3D sensors for depth detection) into a unified motion detection system. By merging these sensors and processing their combined data, the system achieves high measurement precision while managing device complexity through integrated processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves real-time object segmentation with improved speed and reliability by hierarchically triggering segmentation on detected human or non-human motion, optimizing resource usage and enhancing the accuracy of motion classification.
Implementation Method 1
a low resolution passive infrared ("PIR") sensor (104) that measures infrared light that radiates from physical objects within its field of view
Implementation Method 2
a three-dimensional ("3D") sensor (112) that measures light that radiates from physical objects within its field of view, which substantially overlaps the field of view of the PIR sensor (104)
Data Source
AI summary
In response to detecting a motion within a video sequence, a determination is made of whether the motion is a particular type of movement. In response to determining that the motion is the particular type of movement, a location is identified within the video sequence of an object that does the motion.