Detect vehicles speed from CCTV Cameras with Opencv and Deep Learning

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In this tutorial, we will see how to use CCTV camera footage to calculate the vehicle speed detection of each individual vehicle passing on the road

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#SpeedDetection #OpenCV #DeepLearning
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Botak Berguna, terimakasih telah memberikan TUTORIAL. GOOD JOB,

_lowisroyfigo
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Hi,
Would it be possible to detect the speed of vehicles, but with a camera at the same horizontal level as them? That is to say, a view which is not plunging, as in your example.

We have a street; we place a camera on a pole about six feet high. We record passing vehicles and later, using Open CV or other programs, we are able to obtain the speed of everything in the camera's field of view.

Of course, we took the distances beforehand, for example using a 100-foot measuring tape.

Thank you for sharing your knowledge.

CarlDorio
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where can we find the python file named deep_sort.deep_sort that you used in the video?

akifakbulut
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Hi Sergio, can you make a video where it can determine (heavy congestion, moderate congestion, no/light congestion) based on speed/ number of vehicles passing through point 1 to point 2? This might be very helpful for a student like me who wanted to explore more on object tracking. Thank you.

shamrao
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TYSM for this video, I love it. But anyone have the code for execute this? Or using the one´s you let us see on the screen could work? I mean at leas the parts we see for every instance in the video. TYSM

NA-cwpj
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can you please make the video to predict trajectory of multiple objects in different polygon?? i will be thankful to you

arslanmanzoor
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Love it, I really like ur motivation and ideas. I also think that there is a lot of potential in cv for ip cameras

marcomaggiotti
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Your videos helped me a lot, really really thank you

mohammadaliavazpour
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its a great videos
i get mini problem where is the full video that you croped it to implement this project

abdulrahmanalshameery
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sir can we measure the distance ? please answer..

mdkamruzzaman
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Thanks for the video. I want to give my thoughts about this. 16seconds traveling time does not seem correct. I do not think that is a 65 meters distance, it is lower than that. The time that you are calculating is not the actual traveling time, you are calculating the processing time in your computer. You are using deep sort (heavy computation), the processing time is slow, which makes time look longer and in fact that is due to the slow processing. Your timing should be based on the entering and exiting frame rather than time based on processing. You are considering that delay for calculating time here which is not accurate.

happypumpkinpm
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how can we take this implementation to PyTorch yolov5 detection?

anime_on_data
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I need to capture torpedo speeds for my project those it work underwater.

hermitking
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Hi
Thank you for the tutorial!
I am doing similar project but I have some problems when counting objects passing roi. Image distortion in roi causing wrong object counting. I am using nvidia geforce rtx 3090. Help me to fix this problem, please

zy.r.
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sergio, could you please make a video where the camera is not fixed? so the vertexces of the area of interest should get refreshed.
Thanks!

tomasdaels
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Hi Sergio, a question what's the reason of this kind of situation "I have a camera footage from the road, yolo alghoritm does detection quite well, but in length of a few frames exactly same object is detected many times and it's label with number changed" ? Especially it's visible during car passing by.

adampiksel
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a rewarding video. However, do you have datasets on the velocities of the vehicles for ground truth?

loiuc
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kindly upload the vedio which you had uesd

haeemnaeem
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Thaks. I have a question.
How to get and install object_detection and deep_sort?

rakotomamonjypaulmazoto
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V good video - you could add a suggestion to calibrate it by driving the road at a known set speed. Then tweak the estimated distance figure to match

GaryBuck
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