AI Distance Event Detection Using Uniform View Transformation
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Solution Overview
Problem
Existing systems face challenges in accurately determining the distance between objects in images or videos captured from different perspectives or angles, leading to inefficiencies in computing resources, processing, and memory usage, especially when handling multiple cameras, and require users to navigate and access results separately for each camera.
Innovation Solution
An AI-enabled system that transforms images to a uniform view, calculates distances using bounding boxes, and detects events based on thresholds, utilizing a decoupled cloud-based architecture to improve scalability and resource management, providing a unified user interface for multiple cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distance determination is performed for each camera configuration and view separately, then measurement precision is improved, but device complexity and computing resources increase significantly
Solution Approach 1:
The patent implements a universal distance determination approach where a single machine learning model is trained to handle multiple camera configurations, perspectives, and views simultaneously. The model learns to determine distances across diverse shooting conditions (different angles, heights, locations) using one unified system rather than separate models for each configuration, thereby reducing device complexity while maintaining measurement precision.
Solution Approach 2:
The patent transforms the distance determination problem by changing parameters from camera-specific configurations to a unified perspective transformation approach. By converting images to a uniform top-down view and using ratio values derived from reference objects, the system adapts to different camera parameters without requiring separate determination models for each configuration.
2Measurement precision
If separate analysis is performed for images from multiple cameras, then measurement precision is improved, but computing resources and processing time increase
Solution Approach 1:
The patent merges the distance determination process by training a single machine learning model to handle images from multiple cameras simultaneously. Instead of performing separate analyses for each camera, the unified model processes images from all cameras through a single determination operation, reducing computing resource consumption and processing time while maintaining the precision of individual camera measurements.
3Measurement precision
If multiple camera feeds are analyzed separately, then measurement precision is improved, but loss of time occurs due to separate navigation and access for each camera
Solution Approach 1:
The patent creates a universal interface that provides distance determination results for multiple cameras through a single access point. The system presents unified distance information for all cameras in a consolidated display, eliminating the need for users to navigate separately to each camera feed while maintaining the measurement precision that would otherwise require individual camera analysis.
4Measurement precision
If the system is trained for each configuration and view of images, then measurement precision is improved, but productivity decreases due to significant computing and memory resources required
Solution Approach 1:
The patent achieves universal distance determination by training one machine learning model to handle all camera configurations and perspectives. This single model replaces multiple configuration-specific models, significantly improving system productivity by reducing training time, computing resources, and memory requirements while maintaining the measurement precision that would otherwise require separate trained models for each configuration.
Data Source
AI summary
In some implementations, an image processing system may obtain, from one or more cameras, a stream of image frames. The image processing system may detect, using an object detection model, one or more objects depicted in one or more image frames included in the stream of image frames. The image processing system may generate one or more modified images, of the one or more image frames, including indications of detected objects depicted in the one or more image frames. The image processing system may calculate distances between one or more pairs of objects detected in the one or more modified images, the distances being calculated using the indications and a uniform view. The image processing system may detect one or more events based on one or more distances satisfying a threshold. The image processing system may provide a user interface for display that indicates the one or more events.