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Compiler/IWR1642: for people counting when tracking an objective,why we need to unroll RadialVelocity?
Part Number: IWR1642
Tool/software: TI C/C++ Compiler
I am learning people counting's gtrack.I have the following questions:
1.In the prediction step of EKF,
Q is the process noise covariance matrix.And we consider piecewise white noise model for the noise.
We define maxAcceleration 5m/s according to your demo.What is the basis for setting the value?In gtrackUnitPredict function,obj.P_apriori(1:mSize,1:mSize) = obj.F(1:mSize,1:mSize) * obj.P(1:mSize,1:mSize) * obj.F(1:mSize,1:mSize)' + obj.Q(1:mSize,1:mSize)*obj.processVariance
processVariance = (0.5f*maxAcceleration)*(0.5f*maxAcceleration).Why is maxAcceleration multiplied by 0.5?
2.In gtrackUnitScore function,we compute the Mahalanobis distance between all measurements of cloud point and different tracks.
1)We build a gate using gtrack_gateCreateLim function.
gC_inv (EC) is the inverse of group covariance matrix(gC).Sometimes,the main diagonal of the matrix(gC_inv) is negative (gC's is positive).Is it right?Can the gate be negative?
We use gtrack_computeMahalanobis3 function to compute mahalanobis distance.Still,the main diagonal of the matrix(gC_inv) is sometimes negative.
Can the mahalanobis distance be negative?
In reply to Michael_Livshitz:
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In reply to user5877061:
I suggest you focus on the frame where you have second target allocated, and understand the reasons. Your understanding of scoring function and numerical example is correct. M is number of measurement dimensions. Not sure why would you need it. In the end, whether we associate particular point to one or another target that are very close or even overlap is not that important. I.e. if one's hand overlaps another target's shoulder => no big deal if we associate reflection point one way or another.Errors will be made. Ideally, those errors should not lead to system errors.
I still feel a little confused.when the term ln(mod(Ci)) is closer to zero it's more likely to attract points.Then in my example, it should be reversed.track1's ln(mod(Ci)) is -3 and track2's is -2.The distance from one point to two targets is the same,such as 1.Because when the term ln(mod(Ci)) is closer to zero it's more likely to attract points.Then track2 should be more likely to 'attract' this point.That is, when the term ln(mod(Ci)) is a small negative number(far away from zero),it's more likely to attract points.Is it right?
Thank you for your reply.
I am sorry if I had confused you. To make it clear: the smaller the term ln(mod(Ci)) the more likely the point will be used assigned to that distribution. (-3) is obviously smaller than (-2). Same applies to the distance: the smaller the distance, the more likely the assignment. So, in your example, (-3)+1<(-2)+2, hence the point shall be assigned to the first track. Note, "smaller", not "closer to zero". This is how likelihood was derived (at least based on chi-square distribution assumption).
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