Camera Blindspot Detection Using 3D Delivery Scene Models
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Solution Overview
Problem
Cameras installed at properties often have blindspots that prevent them from capturing images of package deliveries, making it difficult to detect and monitor packages that have been delivered to these areas, which can lead to potential damage or theft.
Innovation Solution
A system that uses a camera to build a model of the delivery scene, identifies blindspots, and detects deliveries to these areas by analyzing historical data and external data, triggering actions such as notifications or adjusting the camera's position to monitor the package.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a camera is installed at a door to monitor package deliveries, then the camera can capture images of packages delivered to visible areas, but packages delivered to blindspots cannot be detected or monitored
Solution Approach 1:
The system transitions from two-dimensional image analysis to three-dimensional spatial reasoning by constructing a 3D model of the delivery scene using multiple images captured from different angles. This 3D model enables the system to identify blindspots (areas not visible to the camera) and infer package deliveries that occur in these hidden regions, thereby extending detection capability beyond the camera's direct field of view.
Solution Approach 2:
The system introduces an intermediary 3D scene model that acts as a mediator between the camera's limited visual data and the need for comprehensive delivery monitoring. This model synthesizes information from multiple images and historical delivery data to represent both visible and hidden areas, enabling indirect detection of packages delivered to blindspots through spatial inference rather than direct visual observation.
2Area of stationary object
If the camera's field of view is expanded to cover more areas, then more delivery locations can be monitored, but the camera cannot capture areas that are structurally hidden (blindspots)
Solution Approach 1:
The system creates a virtual copy of the physical delivery environment through 3D modeling. This digital twin represents the spatial layout, visible areas, and blindspots, allowing the system to simulate and analyze delivery scenarios without physically repositioning cameras. The virtual model enables comprehensive monitoring planning while maintaining simple physical camera deployment.
Solution Approach 2:
The system performs preliminary 3D scene reconstruction and blindspot identification before actual package deliveries occur. By pre-processing the spatial environment and identifying areas not visible to the camera, the system can proactively plan monitoring strategies and infer deliveries to hidden locations, rather than attempting to reactively expand camera coverage after problems arise.
3Reliability
If multiple cameras are deployed to eliminate blindspots, then all delivery areas can be monitored, but the system complexity and cost increase significantly
Solution Approach 1:
The system segments the monitoring problem into two distinct components: (1) capturing images of visible areas with a single camera, and (2) inferring deliveries to hidden areas through 3D spatial reasoning and historical data analysis. This segmentation allows the system to achieve comprehensive monitoring reliability without deploying multiple cameras to every blindspot, as the inference mechanism handles hidden areas computationally rather than physically.
Solution Approach 2:
The system replaces the mechanical solution of deploying additional cameras with a computational approach using 3D modeling, image processing, and machine learning algorithms. Instead of physically extending the camera network to cover blindspots, the system uses software-based inference to detect and monitor packages in hidden locations, substituting mechanical complexity with intelligent processing.
4Measurement precision
If the camera captures images at high frequency to detect all deliveries, then detection accuracy improves, but energy consumption and data processing load increase
Solution Approach 1:
The system applies partial action by capturing images only at critical moments (when delivery events are suspected to occur) rather than continuously at high frequency. Combined with the 3D model and historical data, this selective imaging approach achieves sufficient detection accuracy for both visible and blindspot deliveries while significantly reducing energy consumption and data processing requirements compared to continuous high-frequency capture.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting packages delivering in the camera's blindspot. One of the methods includes detecting, using one or more images captured by a camera at a property, movement in an area of interest i) at the property, ii) that is included in a field of view of the camera and iii) was generated using historical data for packages delivered to the property; determining, using the detected movement in the area of interest, that a package was likely delivered; and in response to determining that the package was likely delivered, performing one or more automated actions for the package.