Speed Estimation & Vehicle Tracking | Computer Vision | Open Source

Learn how to track and estimate the speed of vehicles using YOLO, ByteTrack, and Roboflow Inference. This comprehensive tutorial covers object detection, multi-object tracking, filtering detections, perspective transformation, speed estimation, visualization improvements, and more.

Use this knowledge to enhance traffic control systems, monitor road conditions, and gain valuable insights into vehicle behavior.

Chapters:

- 00:00 Intro
- 00:36 Object Detection
- 03:43 Multi-Object Tracking
- 05:11 Filtering Detections with Polygon Zone
- 06:39 Math Behind Perspective Transformation
- 14:35 Perspective Transformation in Code
- 16:46 Math Behind Speed Estimation
- 18:42 Speed Estimation in Code
- 21:29 Visualization Improvements
- 22:45 Final Results

Resources:

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[kemal_kilicaslan]

As a mathematician, your analytical geometry skills are admirable. I've been following your work on image processing applications closely and find it crazy. Keep it up Piotr.

[patricksimo]

Thank you for your efforts. The video is perfect and very well explained. Great work !

[blessingagyeikyem]

Supervision is super useful. I have been using it in my computer vision workflow. I now prefer it over opencv. Keep up with the good work Piotr.

[the_vheed]

Thank yo so much for this video. It greatly simplified the entire speed estimation process

[amirsv]

Crazy how object detection is just getting better and better!

[Studio-gsye]

That is unique type of tutorial I have seen so far and thanks for such a good content.

[smccrode]

This is amazing! Thank you! Been wanting to do this for years. Now I’m going to do it!

[minasamir]

Great work This is amazing! Thank you!

[rluijk]

Great! Thanks for your clear explanations, showing what is possible. Very inspiring. Subscribed so I hope to see more creative tracking concepts explained.

[theoldknowledge]

These application videos are amazing!!

[hoangng]

This is great; I've wanted to do this for a long time.

[tjoec]

Amazing tutorial. Learnt something new today. Thanks a lot.

[tobieabel]

Another great video Piotr! I am currently working on a project using Supervision to track the speed of hand movements as part of a hand gesture recognition system, and your tutorials are really timely. I'm detecting the hands, performing some minor perspective transformation as you do here, tracking their movements within certain zones, and calculating their speed over several frames to determine the specific gesture. One issue I'm noticing is that Byte track has a tendency to lose detections even within a small area, and I was wondering if you have any tips for improving tracking performance other than playing with the byte track parameters?

[minhnguyenquocnhat]

Thank you so much for this tutorial. Your instruction is very great

[cappittall]

Thanks Peter, That is great tutorial. :)

[luisescares]

Congratulations by this video, greatings from Santiago!

[SantiagobgbO]

Your explanations have been incredibly helpful. Thank you sir!

I'm currently working on a project where I apply similar tools to estimate the velocity of tennis players. However, I've encountered a challenge: the players often have part of their bodies outside the designated court polygon, which complicates the tracking. Is it possible to define multiple polygons to capture the full range of their movements, or do you have any recommendations for this scenario?

Thank you once again for your valuable contribution to the community! <3

[elhadjikarawthiam]

Thank you very much for sharing, it’s really interesting. I would like support for my subject on the analysis of congestion up to measuring the distance of traffic jams

[Scarfy]

One of the best channels... I love u piotr

[kimridaaa]

terimakasih bang bule, thankyou sm brok buleee