I want to develop a python script that takes as input a video recording from a dashcam and estimates the speed of the cars in the video.
as I put you will also have the second by second speed, gps coordinates, and gyroscope measurements of the dashcam (mounted inside my car as I'm driving).
Basic method to solving the problem:
- retrain YOLOv3 to also detect licence plates in image. there are many open source licnese plate datasets out there. Yolo is already implelemeted in tensor flow in many open source github repos. you can use any of them.
- draw a bounding bos around each license plate.
- use edge detector to detect 4 edges of license palte.
- you will get real licnese plate dimensions (width and height in cm) as well as focal length of camera as input as well.
- use geometry to figure out coordinates of target license plate in 3d space for each frame. then detect centered of licnese plate.
- you will get frame rate of input video also. you can use this information to calculate speed of licnese plate from frame to frame.
- output in Csv or json format saying coordinates of 4 corners of each license plate, frame number, and estimated speed.
20 freelance font une offre moyenne de $666 pour ce travail
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Hi, I am an expert in Image processing, OCR and computer vision. I developed car speed determining app. In my app, the accuracy of speed is 98% and the accuracy of license plate is 99%. If you send the your video, I Plus
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I have done project related to YOLO 3, and more specifically for traffic analysis, object detection and clarification like if it's a car, bike or trunk. The only thing that I'm not familiar with is speed recognition of Plus
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