Object Detection Coverage Estimation Using 3D Spatial Mapping
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Solution Overview
Problem
Conventional computer vision systems face challenges in providing accurate real-world spatial coverage information due to system constraints and trade-offs, leading to incomplete object detection and decision-making difficulties.
Innovation Solution
A method and system for object detection coverage estimation that determines spatial coverage of detected objects using metadata and a pinhole camera model to calculate three-dimensional coordinates from two-dimensional image data, identifying both coverage areas and blind spots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional methods reduce computing power requirements by lowering frame rate, reducing image resolution, or limiting detection parameters, then computing power requirements are reduced, but the effective processing coverage and detection accuracy deteriorate
Solution Approach 1:
The patent introduces a new dimension of spatial coverage estimation by calculating three-dimensional coverage areas from two-dimensional detection data. This allows the system to maintain full-resolution processing while providing additional spatial context that helps optimize detection parameters without sacrificing accuracy. The coverage estimation operates in a third spatial dimension, enabling better decision-making about where to apply detection resources.
Solution Approach 2:
The system implements feedback by using estimated spatial coverage information to inform subsequent detection decisions. The coverage estimation provides feedback about which areas are already well-covered versus which areas need more detection attention, allowing the system to dynamically adjust detection parameters and resource allocation to maintain accuracy while reducing overall computing requirements.
2Adaptability or versatility
If the system reconfigures software within system constraints to fulfill requirements, then system requirements are met, but the effective processing coverage is reduced and the system can no longer cover the full area of the camera's view
Solution Approach 1:
The patent adds a coverage estimation dimension that operates independently of the detection configuration constraints. By estimating three-dimensional spatial coverage from two-dimensional detection results, the system can evaluate the actual coverage achieved regardless of software reconfiguration, allowing requirements to be met without permanently losing coverage area.
Solution Approach 2:
The coverage estimation acts as an intermediary between system constraints and actual detection performance. It provides objective measurement of coverage that mediates between the conflicting requirements of adapting to system constraints and maintaining full processing coverage, enabling informed decisions about configuration trade-offs.
3Measurement precision
If conventional object detection systems process images at full resolution with high frame rates, then detection accuracy is improved, but computing power requirements increase significantly
Solution Approach 1:
The patent introduces spatial coverage estimation as an additional dimension that works alongside traditional detection. This coverage information enables smarter resource allocation by identifying which high-resolution processing is actually necessary versus which areas could use lower-resolution processing, thereby reducing overall computing power while maintaining accuracy where it matters.
Solution Approach 2:
The system uses coverage estimation to dynamically change processing parameters based on actual spatial coverage needs. Areas with sufficient coverage can use lower computing power settings, while areas needing better detection maintain high-resolution processing, optimizing the balance between accuracy and power consumption across the entire field of view.
Data Source
AI summary
Methods, systems and computer readable medium for object detection coverage estimation are provided. The system for object detection coverage estimation includes a camera and a processing means. The processing means is coupled to the camera to receive image data acquired by the camera, the image data including a detected object. The processing means is configured to determine a spatial coverage of the detected object based on detected object metadata associated with the detected object in the image data received from the camera.