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These are the calibration results for each camera using HDRShop.
The mathematical model of the stereo vision system with 10 distortion parameters for each camera is proposed.
However, there is a need for having decentralized systems capable of pre-processing data for each camera node in order to reduce the network traffic, and increase privacy (especially important in wireless networks).
The model was deployed on an actual binocular vergence system with independent pan-tilt controls for each camera and the system was able to robustly verge on objects even in cluttered environment with real time performance.
That reflects simple physics: a lump of metal going fast will hurt you more than one going slowly.There are plans to publish data for each camera, showing accident rates before and after.
Device ID is the unique identification for each camera device.
Each point stands for each camera with adapted reconstruction parameter.
Therefore, only two images are acquired every day, one for each camera.
Then, calibration data is used to generate virtual images of the evaluation scene for each camera.
The existence probabilities for each camera are predicted using the binary images from the background subtraction.
We exclude distant regions by specifying an ROI for each camera.
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