Video Surveillance Distortion Correction for Accurate Object Tracking
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Solution Overview
Problem
Existing video surveillance systems face difficulties in accurately processing video streams due to varying lens distortions caused by different camera lenses, which affect the monitoring and tracking of objects within the monitored area.
Innovation Solution
A method is provided to enhance video surveillance by identifying and correcting lens distortions in video streams using user input to correlate objects in the video stream with a map, determining distortion types, and monitoring object movements to improve tracking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If fisheye lens or wide-angle lens is used to capture broader view, then area of interest coverage is improved, but image distortion increases
Solution Approach 1:
The system dynamically adjusts processing parameters based on the detected lens type and distortion characteristics. By identifying the specific distortion pattern (barrel, pincushion, or fisheye) and applying corresponding correction algorithms with adjusted strength parameters, the system maintains accurate object tracking across the entire wide field of view while compensating for the inherent geometric distortions introduced by wide-angle and fisheye lenses.
2Area of stationary object
If different lens designs are used to capture wider perspective, then field of view is improved, but object tracking accuracy deteriorates
Solution Approach 1:
The system segments the video frame into multiple zones with different distortion characteristics, applying zone-specific correction factors during object tracking. By dividing the wide field of view into regions with relatively uniform distortion patterns and processing each region independently with appropriate correction parameters, the system maintains consistent tracking accuracy across the entire expanded field of view that would otherwise be unachievable with standard lenses.
Solution Approach 2:
The system replaces physical mechanical correction methods (such as using multiple cameras or complex lens assemblies) with computational correction algorithms. By using image processing techniques to mathematically reverse the distortion effects introduced by wide-angle and fisheye lenses, the system achieves accurate object tracking across wide fields of view without requiring additional hardware complexity.
3Measurement precision
If distortion correction is applied to improve tracking accuracy, then object location precision is improved, but processing complexity increases
Solution Approach 1:
The system performs preliminary distortion correction by pre-calculating and storing correction lookup tables specific to each lens type during system initialization. By pre-processing the distortion correction parameters and creating reference maps that relate distorted pixel coordinates to corrected coordinates, the system eliminates the need for complex real-time calculations during object tracking, thereby maintaining high location precision while minimizing processing complexity and computational overhead during operation.
Data Source
AI summary
Systems, methods, and software to process video surveillance data based on distortion identified in video streams. In one example, a computing device identifies a video stream from a video source and a map of a physical area monitored by the video source. The computing device further obtains user input identifying objects in the video stream and the map, wherein the user input for each of object of the objects correlates a tag of said object in the video stream to a tag of a representation of said object in the map. From the user input, the computing device calculates distortion in the video stream and monitors movement trends associated with additional objects based on the determined distortion, the user input, and the movement of the additional objects in the video stream.