Neural Network Speed Estimation Without Camera Calibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing camera-based traffic monitoring systems require accurate calibration and assume flat, straight road geometry, limiting their effectiveness and scalability, especially for uncalibrated CCTV cameras.
Innovation Solution
A data-driven approach using a neural network to determine local mappings between pixel coordinates and distance coordinates without global parameters, employing Jacobians to convert pixel displacements to distance, enabling speed estimation without camera calibration and road geometry assumptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera-based traffic monitoring systems are used, then speed estimation can be achieved, but accurate calibration and road geometry assumptions are required
Solution Approach 1:
The system uses vehicles themselves as calibration references by detecting their known physical dimensions (length, width) and using them to automatically determine scale factors and road geometry parameters, eliminating the need for external calibration equipment or procedures
Solution Approach 2:
The system dynamically estimates camera parameters (focal length, principal point) and road geometry parameters (slope, curvature) by analyzing vehicle appearances at different positions in the image sequence, allowing the system to adapt to various road conditions without manual calibration
2Adaptability or versatility
If traditional camera systems are used for traffic monitoring, then speed estimation is possible, but they are limited to flat and straight roadways
Solution Approach 1:
The system dynamically models the road geometry as a 3D surface with varying slope and curvature along the roadway, allowing it to accurately represent and compensate for non-planar road shapes rather than assuming a static flat surface
Solution Approach 2:
The system transitions from 2D image coordinates to 3D world coordinates by incorporating road elevation and curvature information, enabling accurate speed estimation on inclined and curved roadways by accounting for the additional spatial dimensions
3Productivity
If calibrated camera systems are deployed, then speed monitoring can be performed, but installation is expensive and time-consuming
Solution Approach 1:
The system performs automatic self-calibration by utilizing vehicles already present in the scene as reference objects, extracting calibration parameters directly from the video feed without requiring external calibration equipment, manual measurements, or specialized installation procedures
Solution Approach 2:
The system uses standard off-the-shelf cameras without specialized calibration hardware or expensive infrastructure modifications, accepting that the camera may be uncalibrated or移动的, and relying on software-based compensation methods that work with conventional equipment
Data Source
AI summary
A speed estimation system includes: a detection module having a neural network configured to: receive a time series of images, the images including a surface having a local geometry; detect an object in the time series of images on the surface; determine pixel coordinates of the object in the time series of images, respectively; determine bounding boxes around the object in the time series of images, respectively; determine local mappings, which are not a function of global parameters describing the local geometry of the surface, between pixel coordinates and distance coordinates for the time series of images based on the bounding boxes around the object in the time series of images, respectively; and a speed module configured to determine a speed of the object traveling relative to the surface based on the distance coordinates determined for the time series of images.