Understanding Sensor Fusion and Tracking, Part 4: Tracking a Single Object With an IMM Filter

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Check out the other videos in the series:
Part 1 - What Is Sensor Fusion?: • Understanding Sensor F...
Part 2 - Fusing an Accel, Mag, and Gyro to Estimation Orientation: • Understanding Sensor F...
Part 3 - Fusing a GPS and IMU to Estimate Pose: • Understanding Sensor F...
Part 4 - Tracking a Single Object With an IMM Filter: • Understanding Sensor F...
Part 5 - How to Track Multiple Objects at Once: • Understanding Sensor F...
This video describes how we can improve tracking a single object by estimating state with an interacting multiple model filter. We will build up some intuition about the IMM filter and show how it is a better tracking algorithm than a single model Kalman filter.
We cover what makes tracking a harder problem than positioning and localization because there is less information available to the tracking filter. We explain how the IMM makes up for the lack of information, and show some simulated results.
Check out these other references!
Tracking Maneuvering Targets - Example: bit.ly/2MKu4Om
The mathematics behind IMM: bit.ly/2J1Fcpl
Kalman Filters - Overview: bit.ly/2Me89QJ
Multi-Object Tracking for Autonomous Systems and Surveillance Systems - Ebook - bit.ly/3fChIV1
Multi-Object Tracking: bit.ly/3mMPH0s
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14 июл 2024

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Комментарии : 18

@ZardoshtHodaie 2 года назад

The beauty of the math only becomes clear when a good teacher teaches it. Thank you! I truly wished there were more teachers like you at every level of education.

@unomasenelmar 4 года назад

Oh!, It has left me speechless, magnificent explanation in the university would have delayed in explaining the same years you explain it in minutes. great video. Congratulations!

@m.dawoodowais9514 3 года назад

Man it takes me more than an hour to properly go through each video but its deffo worth it. Great stuff.

@alanoorchannel4469 4 года назад

Thanks Brian ... excellent lecture ... make us understand the complicated think very easily

@dcpowered 4 года назад

I love Brian's videos. Excellent explanation as usual! Please more!!!

@BrianBDouglas 4 года назад

Thanks!

@dhinas9444 2 года назад

I love, how I always resort back to your videos when trying to wrap my head around various control engineering problems. I just love how you explain everything with ease, clear graphics, and answer questions just as they pop up in my head. Thank you for all the work! I also loved the PID videos!

@volodymyrhavrylov7993 3 года назад

I really loved the explanations in this series of videos. Thanks for a great content! I hope you would get more views, as the sensor fusion topic is quite important for anybody who works with autonomous systems, and the number of engineer which work with it grows day by day.

@lucasauerdearaujo5234 3 года назад

Man you are incredible

@qamarkilani551 4 года назад

Thanks Brian for your great explanation ,.. which books you recommend for further readings ?

@francescocolombi9021 3 года назад

Hi Brian, hi all ^^ What do you think about adding to the filtering process an interpolation of the last "n" estimated states so that you can add a prediction also on the interpolation of the states?

@gregg9475 2 года назад

Is the predicted and measured state delta an accuracy error?

@sdsachin24 Год назад

How does Normalized distances are calculated in this video ?

@choungyoungjae8271 2 года назад

11:30 12:23 IMM

@mdineen7149 4 года назад

How would you connect this system to a real camera?

@francescocolombi9021 3 года назад

I've never applied that, but I guess you have to link in some way the input of the cameras (by processing the input images) to your states (attitude, position, ...). Then it works in the same way you have a radar like in the example of this video.

@Roborob12345 5 месяцев назад

You can use one of MATLABs image detection algorithms and find the centre of the located object, doing this on 2 cameras spread apart would allow you to measure the difference in angle and build a triangle to solve for the distance of the object from the sensors. That’s Subaru Eyesight in a nutshell.