Vehicle Surveillance Using Depth Maps for Low-Power Proximity Warnings
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Solution Overview
Problem
Existing vehicular security systems are inaccurate and inefficient in detecting potential threats and consume excessive power, particularly when parked or idle.
Innovation Solution
A vision-based system using onboard cameras to detect moving objects, determine their size and proximity, and trigger warnings or notifications, potentially activating additional sensors for further information, thereby enhancing security and reducing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor-based security systems are used, then detection capability is provided, but power consumption is excessive and detection accuracy is insufficient
Solution Approach 1:
The system implements periodic action by capturing images at defined intervals rather than continuously, and by activating additional sensors only when motion is detected. This intermittent operation mode significantly reduces power consumption while maintaining effective surveillance coverage through regular monitoring cycles.
Solution Approach 2:
The system replaces traditional mechanical sensor-based detection with vision-based detection using image capture devices. This substitution enables more accurate object detection and classification capabilities while reducing power consumption through intelligent image processing and selective sensor activation based on visual cues.
2Reliability
If continuous monitoring is implemented, then security coverage is improved, but power consumption increases
Solution Approach 1:
The system employs periodic monitoring by capturing images at predetermined intervals and activating additional sensors only when motion is detected in captured images. This approach maintains reliable security coverage through regular monitoring while consuming significantly less power compared to continuous monitoring of all sensors.
Solution Approach 2:
The system implements self-service through intelligent image processing that automatically detects motion and triggers selective sensor activation. The vision-based system serves as the primary detector, eliminating the need for continuous operation of additional sensors and enabling power-efficient security monitoring through automated decision-making.
3Measurement precision
If multiple sensors are activated continuously, then detection accuracy is improved, but system complexity and power usage increase
Solution Approach 1:
The system reduces complexity by activating additional sensors periodically only when motion is detected in captured images, rather than keeping all sensors continuously active. This selective activation maintains high detection accuracy when needed while simplifying system operation and reducing power consumption during normal monitoring periods.
Solution Approach 2:
The system replaces complex continuous multi-sensor operation with a simplified vision-based detection system that intelligently triggers additional sensors only when necessary. This substitution reduces system complexity by using image processing as the primary detection mechanism while maintaining accurate object detection and classification capabilities.
Data Source
AI summary
Disclosed systems and methods include receiving an image, processing the image with a depth estimation module to generate a first depth map, generating a foreground mask of the image, inpainting regions of the image, generating a second depth map based on the inpainted regions of the image, comparing histograms of the first and second depth maps, and generating proximity warnings based on the comparison of the histograms.