Virtual Tripwire Placement Using Traffic Direction Variance
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Solution Overview
Problem
Conventional manual selection of tripwires for traffic monitoring is arbitrary and not representative of optimal locations for analyzing traffic flow, leading to suboptimal data collection for traffic management decisions.
Innovation Solution
An automated method determines the optimal placement of virtual tripwires by analyzing series of images to identify objects of interest, calculating their direction and variance of travel, and minimizing travel variance through a sliding window approach, using aerially mounted nodes with video cameras and processors to automatically select the best placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of tripwires is used for traffic monitoring, then implementation is simple, but the locations are arbitrary and not representative of optimal locations for analyzing traffic flow
Solution Approach 1:
The system performs self-service by automatically analyzing video data to determine optimal tripwire locations without requiring manual intervention. The processor autonomously identifies objects of interest, calculates travel directions and variances, and selects the best tripwire placement based on the data, making the system self-configuring and adaptive to actual traffic patterns.
Solution Approach 2:
The system performs preliminary action by pre-analyzing video footage to identify optimal tripwire locations before actual traffic monitoring begins. By processing historical video data and calculating object travel patterns in advance, the system prepares the optimal configuration, ensuring accurate representation of traffic flow from the start of monitoring operations.
2Measurement precision
If automated analysis of video data is performed to determine optimal tripwire locations, then measurement precision improves, but computational complexity and processing time increase
Solution Approach 1:
The system applies segmentation by dividing the video frame into a grid of points and processing each grid point independently to calculate travel directions and variances. This segmentation allows the complex computational task to be broken down into manageable units, enabling parallel processing and reducing overall computational complexity while maintaining high measurement precision.
Solution Approach 2:
The system uses partial action by focusing computational resources only on relevant portions of the video data - specifically analyzing movements at grid points where objects of interest are detected. Rather than processing the entire video frame uniformly, the system concentrates analysis on areas with actual traffic activity, reducing unnecessary computational overhead while maintaining accuracy.
3Productivity
If automated tripwire placement is implemented, then productivity of traffic monitoring setup improves, but device complexity increases
Solution Approach 1:
The system replaces manual mechanical processes with automated computational methods. Instead of physically installing tripwires at manually selected locations, the system uses video analysis and algorithmic processing to automatically determine optimal placements, eliminating the need for manual site surveys and physical installation decisions, thereby dramatically improving deployment productivity.
Solution Approach 2:
The system achieves universality by designing a multi-functional processor that can perform multiple tasks: object detection, trajectory analysis, variance calculation, and tripwire location selection. This single integrated system handles all aspects of automated tripwire placement, reducing the need for separate specialized devices and simplifying the overall system architecture despite the advanced capabilities.
Data Source
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AI summary
One or more computing devices perform a method for placing a virtual tripwire onto grid points of a geocoordinate grid. A device processor directs a camera to capture a series of images within the camera's field of view. The processor identifies objects of interest within the images and determines movements of the objects through the images. The processor identifies directions of travel of the objects and determines variances in those directions of travel. The processor further determines vectors of median direction of travel for vector points corresponding to the objects on the grid. The processor then determines multiple candidate locations for placement of the tripwire across grid points of the grid in a direction approximately orthogonal to the direction of travel of at least one object of interest and automatically selects one of the candidate locations based on the vectors of median direction of travel and the travel direction variances.