Static Camera Self-Calibration Using Vehicle Geometry
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Solution Overview
Problem
Existing surveillance systems with static cameras face challenges in self-calibration, relying on assumptions about known objects, which limits their accuracy in estimating vehicle sizes and distances traveled.
Innovation Solution
A method and system that automatically calibrate static cameras using vehicle information from captured images, determining 2D projected shapes, sizes, locations, and features to infer geometry scenes, allowing for the estimation of camera parameters such as height, tilt, and focal length.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If self-calibration is based on assumptions about known objects, then the calibration process is simplified, but the accuracy of estimating vehicle sizes and distances deteriorates
Solution Approach 1:
The system performs self-calibration by automatically detecting vehicles in the scene and using their detected positions, sizes, and characteristics to compute camera parameters without requiring manual input or pre-known object information. The calibration process serves itself by utilizing the surveillance data it already captures.
Solution Approach 2:
The system changes the calibration approach from relying on fixed assumptions about known objects to dynamically computing camera parameters (focal length, height, tilt) based on detected vehicle parameters. This allows the system to adapt to different scenes and vehicle types while maintaining accuracy.
2Ease of manufacture
If traditional calibration methods are used, then calibration can be performed with known objects, but the system lacks adaptability to different vehicle types and scenarios
Solution Approach 1:
The calibration system becomes universal by detecting and utilizing multiple types of vehicles (cars, trucks, buses) as calibration references. The system can handle various vehicle types, sizes, and positions in different surveillance scenarios without requiring scenario-specific calibration procedures.
Solution Approach 2:
The system automatically adapts to different vehicle types by detecting their characteristics in the scene and using them for calibration, eliminating the need for manual configuration or pre-programming of vehicle-specific parameters.
3Measurement precision
If manual calibration procedures are employed, then accurate camera parameters can be obtained, but the process requires human intervention and time
Solution Approach 1:
The system performs calibration automatically using vehicles that are already present in the surveillance scene, eliminating the need for separate calibration procedures. The calibration happens concurrently with normal surveillance operations, saving time and resources.
Solution Approach 2:
The system conducts self-calibration without human intervention by automatically detecting vehicles, extracting their parameters, and computing camera calibration values, thereby eliminating manual calibration time while maintaining accuracy.
Data Source
AI summary
A method to automatically calibrate a static camera in a vehicle is provided. The method may include receiving a captured image file. The method may also include detecting a plurality of vehicles in the captured image file. The method may further include determining a 2D projected shape, size, location, direction of travel, and a plurality of features for each vehicle at various locations in the captured image file. The method may additionally include inferring a plurality of geometry scenes associated with the captured image file, whereby the plurality of geometry scenes is inferred based on the determined 2D projected shape, size, location, direction of travel, and the plurality of features of each vehicle within the detected plurality of vehicles as projected onto the captured image file. The method may include calibrating the static camera based on the inferred plurality of geometry scenes associated with the captured image file.