Video Speed Measurement Using Wheelbase Scaling
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Solution Overview
Problem
Existing methods for measuring vehicle speed from video captures are limited by the need for precalibration, reliance on camera lens knowledge, vehicle distance estimation, and fixed position markers, leading to inaccuracies and increased costs.
Innovation Solution
A method that calculates vehicle speed by determining the time elapsed between a vehicle's front and rear wheels reaching a reference position in an image, using the wheelbase as a scaling factor, without requiring knowledge of the camera lens, vehicle distance, or fixed position markers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If doppler shift method with lidar or radar sensor is used, then vehicle speed measurement accuracy is improved, but device cost and complexity increase
Solution Approach 1:
The patent extracts the speed measurement function from complex doppler shift sensors (lidar/radar) and implements it using only video capture and image processing algorithms. The speed measurement capability is separated from the need for expensive sensors, achieving accurate speed measurement through pure computer vision techniques by tracking vehicle position across video frames and calculating speed from positional changes over time.
Solution Approach 2:
The patent replaces the mechanical/physical sensor system (lidar/radar doppler shift method) with an optical-computational system (video capture with image processing). Instead of using electromagnetic wave reflection and frequency shift detection, the system uses video frame analysis, vehicle position tracking, and temporal distance measurement to calculate speed, substituting a complex physical measurement system with a computational imaging approach.
2Measurement precision
If doppler shift method with lidar or radar sensor is used, then vehicle speed measurement accuracy is improved, but deployment flexibility is limited
Solution Approach 1:
The patent makes the speed camera system universal by eliminating the need for specialized doppler shift sensors. The video capture-based speed measurement can be implemented with standard cameras in various deployment scenarios (fixed locations, mobile platforms, different environments) without requiring specific sensor conditions, thus achieving both accurate speed measurement and deployment flexibility.
Solution Approach 2:
The patent enables dynamic deployment by removing fixed infrastructure requirements. The system can be deployed in fixed or mobile locations, on various platforms, and in different environmental conditions, adapting to diverse deployment scenarios while maintaining accurate speed measurement through software-based processing rather than hardware-based sensor requirements.
3Measurement precision
If fixed position markers are used for speed measurement, then speed measurement from video is possible, but setup cost and complexity increase
Solution Approach 1:
The patent extracts the speed reference information from external fixed markers and obtains it automatically from the video stream itself by detecting and tracking vehicle features (such as wheel positions, vehicle length) within the video frames. This eliminates the need for physical marker installation while maintaining the ability to calculate speed from video analysis.
Solution Approach 2:
The system performs self-calibration and self-measurement by using the vehicle's own visual features (dimensions, position changes) as reference for speed calculation. The vehicle itself provides the measurement reference through its visible characteristics in the video, eliminating the need for external calibration markers or infrastructure.
4Ease of operation
If distance estimation to vehicle is used, then translation from pixels per second to meters per second is possible, but measurement accuracy decreases
Solution Approach 1:
The patent uses vehicle-specific visual features (wheelbase, vehicle length, wheel position) as an intermediary reference that bridges the gap between pixel measurements and real-world dimensions. Instead of relying on distance estimation from the camera, the system uses the vehicle's own known or measurable physical features visible in the video to establish the pixel-to-meter conversion ratio, thereby achieving accurate speed calculation without distance estimation errors.
Data Source
AI summary
A method for measuring the speed of a vehicle from a video motion capture by using the time elapsed between a first wheel of the vehicle reaching a reference position in the image and a second wheel of the vehicle reaching the reference position in an image.