Vehicle-Infrastructure Sensor Fusion for Hidden Object Detection
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Solution Overview
Problem
Security cameras are unable to detect objects that are obscured by vehicles within their field of view, leading to security vulnerabilities.
Innovation Solution
A system that combines data from vehicle sensors with data from infrastructure sensors to create a comprehensive visualization of the obscured region, making the vehicle transparent in the image by replacing its obstructed portion with the actual region of interest using image processing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a security camera monitors a region of interest, then security coverage is provided, but objects blocked by vehicles cannot be detected
Solution Approach 1:
The system merges data from infrastructure sensors (security cameras) and vehicle sensors (cameras, LIDAR, radar) to create a composite view of the region of interest. By combining multiple data sources, the system overcomes the limitation of single-camera occlusion and achieves reliable monitoring even when vehicles block parts of the view.
Solution Approach 2:
The vehicle acts as an intermediary by using its own sensors to capture data about objects that would otherwise be blocked from the infrastructure camera's view. The vehicle's sensor data serves as a mediator to reveal information about obscured objects, which is then integrated with the infrastructure sensor data.
2Loss of information
If vehicle sensors are used to capture obscured regions, then complete monitoring is achieved, but system complexity increases
Solution Approach 1:
The system is designed to work with multiple types of sensors (cameras, LIDAR, radar) and multiple data sources (infrastructure and vehicle sensors) through a universal sensor fusion framework. This multi-functionality allows the system to handle various sensor configurations and occlusion scenarios without requiring separate specialized systems.
Solution Approach 2:
The system creates a virtual copy or representation of the region of interest by integrating vehicle sensor data into the infrastructure sensor's view. This digital reconstruction allows the system to visualize and analyze obscured areas without physically moving cameras or sensors, simplifying the overall system architecture.
3Measurement precision
If multiple sensors are integrated for comprehensive monitoring, then detection capability improves, but processing requirements increase
Solution Approach 1:
The sensor fusion process is segmented into distinct stages: data collection from multiple sensors, data association and matching, occlusion detection, and result integration. This segmentation allows computational tasks to be distributed and optimized at each stage, reducing overall processing requirements while maintaining high detection precision.
Solution Approach 2:
The system applies sensor fusion selectively based on detected occlusion conditions rather than continuously processing all sensor data at full capacity. When no occlusion is detected, the system uses only infrastructure sensor data. When occlusion is detected, vehicle sensor data is activated and integrated, optimizing computational resource usage.
Data Source
AI summary
A vehicle that can augment data captured by an infrastructure sensor, such as a security camera, is disclosed. The vehicle includes processors, a memory, a communication interface, and one or more sensors coupled. The vehicle may receive a first image of a region of interest. The first image depicts that a first portion of the vehicle is obscuring a first portion of the region of interest. The vehicle may also receive a request to capture an image of the region of interest. The vehicle captures a second image of the first portion of the region of interest and generates, using the first image and the second image, a combined image. In the combined image the first portion of the vehicle in the first image is replaced with the first portion of the region of interest from the second image.