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I'm doing some work on tracking moving objects using a ceiling mounted downward facing camera. I've got to the point where I can detect the position of the desired object in each frame.

I'm looking into using a Kalman filter to track the object's position and speed through the scene and I've reached a stumbling block. I've set up my system and have all the required parts of the Kalman filter except the measurement variance.

I want to be able to assign a meaningful variance to each measurement to allow the correction phase to use the new information in a sensible manner. I have several measures assigned to my detected objects which could in theory be useful in determining how accurate the position should be and it seems logical to try and combine them to derive a suitable variance.

Am I approaching this in the right manner and if so, can anyone point me in the right direction to continue?

Any help greatly appreciated.

Chris
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2 Answers2

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sorry for the late reply. I have personally encountered the same problem in my previous project. I found the advice given by Gustaf Hendeby in his Sensor Fusion lecture slides ( Page 10 of the slides) extremely valuable.

To summarize:

(1) The SNR of your measurement noise and your process noise determines your filter behavior. A high process noise/measurement noise ration makes your filter slower (low-pass filter), which will usually allow smoother tracking, vice versa a if you set your measurement noise low, you essentially have a high pass filter, which tends to have more jitter.

(2) There are numerous papers in the literature discuss on how to set these noise model properly. However, usually a lot of "tuning" is needed depends on your application. Usually the measurement noise is what we can measure/characterize based on the hardware specification. Therefore a recommendation is to fix "R" (measurement noise covariance) and tune Q (the process model noise covariance).

SunnyIsaLearner
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I think you are right. According to this post:
Sensor fusioning with Kalman filter
determining the variance is 100% experimental. It seems to me you have everything you need to get good estimates of the variance.

Community
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Ali
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  • Thanks - seems as though I'm on the right tracks then. Just need to find a function which maps the stuff I know about my targets into sensible variance measures then... – Chris Mar 24 '11 at 13:50