Dual-Camera Drive-Through Tracking via Feature Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for assessing customer ordering in drive-throughs face challenges in accurately detecting and tracking vehicles and customers, leading to errors in ordering time assessment and resource allocation.
Innovation Solution
A video surveillance system utilizing two laterally arranged cameras oriented towards the side windows of vehicles, combined with computer vision methods, to detect and match features of customers and vehicles across camera views, thereby reducing errors in time measurement and ordering process analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single camera is used to detect vehicles in drive-through, then the system complexity is reduced, but the measurement precision of ordering time deteriorates due to difficulty in accurately tracking vehicle movement between different positions
Solution Approach 1:
The monitoring area is segmented into multiple zones, each covered by a dedicated camera. The first camera monitors the first area where vehicles enter, and the second camera monitors the second area where vehicles proceed. This segmentation allows each camera to focus on a specific portion of the drive-through, improving detection accuracy and enabling precise measurement of vehicle transit time between areas without requiring a single complex omnidirectional camera system.
Solution Approach 2:
The patent introduces an intermediary processing system that receives video data from multiple cameras, detects vehicle appearances in different areas, and computes transit times. This intermediary system acts as a mediator between the physical camera array and the final measurement output, coordinating the data from multiple sources to produce accurate ordering time measurements while managing the complexity of multi-camera synchronization and vehicle tracking.
2Reliability
If multiple cameras are deployed to improve vehicle detection accuracy, then the reliability of customer tracking improves, but the device complexity and cost increase
Solution Approach 1:
Each camera is positioned and oriented to provide optimal local coverage of its specific monitoring area. The first camera is configured to best capture vehicles in the first area, while the second camera is configured for the second area. This local optimization ensures high detection reliability in each zone without requiring every camera to perform all functions, thereby reducing overall system complexity while maintaining high reliability.
Solution Approach 2:
The system transitions from single-point detection to multi-dimensional spatial monitoring by deploying cameras at different locations and angles. This dimensional approach allows the system to track vehicles through multiple spatial points, improving reliability of detection and tracking while distributing the complexity across multiple simple camera units rather than requiring a single complex system.
3Measurement precision
If detailed feature matching is performed between camera views to reduce tracking errors, then the measurement precision of ordering time improves, but the loss of time for processing increases
Solution Approach 1:
The system performs preliminary detection of vehicle features and characteristics when vehicles are first captured by the cameras. By pre-processing and storing key identification features during the initial detection phase, the system reduces the computational burden during the matching phase, thereby improving measurement precision without excessive processing time delays.
Solution Approach 2:
The patent applies partial feature matching rather than complete image analysis. By selecting and matching only the most discriminative features (such as vehicle color, shape, distinctive markings) rather than processing entire video frames in detail, the system achieves sufficient identification precision while minimizing processing time and computational resources required.
Data Source
AI summary
Video surveillance system for assessment of customer ordering in a drive-through, wherein the video surveillance system having a first camera, a second camera, a network, a control unit, a computer vision unit. The first camera acquires a first image, the second camera acquires a second image. The computer vision unit has a calculating features for the first and second images. The computer vision unit a means for matching such features, wherein a vehicle is tracked by matching the first image and second images. The control unit computes a time span between the appearance of the vehicle appearing in the first image and appearing in the second image. The first and second cameras are arranged laterally to a side window of the vehicle, so that inside the vehicle is recorded at two time points.