questions about PredictNextPosition

[FieldsYeh]

first question regard variable prev and current， seems current is prev， prev is current
prev := len((*b).Track) - 1
current := prev - 1

second question in below loop
for i := 1; i < int(account); i++ {
deltaX += (((*b).Track)[current].X - ((*b).Track)[prev].X) * i
......
the value ((*b).Track)[current].X - ((*b).Track)[prev].X seems can compute before the loop

[FieldsYeh]

I've found a c++ version, seem more correct, see below URL
https://github.com/MicrocontrollersAndMore/OpenCV_3_Car_Counting_Cpp/blob/master/Blob.cpp

[LdDl]

first question regard variable prev and current， seems current is prev， prev is current
prev := len((*b).Track) - 1
current := prev - 1

second question in below loop
for i := 1; i < int(account); i++ {
deltaX += (((*b).Track)[current].X - ((*b).Track)[prev].X) * i
......
the value ((*b).Track)[current].X - ((*b).Track)[prev].X seems can compute before the loop

1. Well, notations is not obvious at all. I'll work on it.
2. Is delta really needed to be computed on initial step (i = 0 leads to the same) ? What do you think?

[LdDl]

I've found a c++ version, seem more correct, see below URL
https://github.com/MicrocontrollersAndMore/OpenCV_3_Car_Counting_Cpp/blob/master/Blob.cpp

I've used to use this code in early implementations, but I don't like hardcoded if/else statements at all.

[FieldsYeh]

below is my modified code.

func (b *KalmanBlobie) PredictNextPosition(n int) {
	account := min(n, len((*b).Track))
	current := len((*b).Track) - 1
	prev := current - 1
	var deltaX, deltaY, sum = 0, 0, 0
	for i := 1; i < int(account); i++ {
		deltaX += (((*b).Track)[current].X - ((*b).Track)[prev].X) * (account - i)
		deltaY += (((*b).Track)[current].Y - ((*b).Track)[prev].Y) * (account - i)
		sum += i
		current = prev
		prev = current - 1
	}
	if sum > 0 {
		deltaX /= sum
		deltaY /= sum
	}
	(*b).PredictedNextPosition.X = (*b).Track[len((*b).Track)-1].X + deltaX
	(*b).PredictedNextPosition.Y = (*b).Track[len((*b).Track)-1].Y + deltaY
}

[LdDl]

I've just updated your message for proper Go-code formatting if you don't mind.

So how does it perform against current implementation?

I need take a deeper look on this code. I think it is good idea to do tests with some drawing (for proof of conpect you know).... Something like this one https://github.com/LdDl/kalman-filter#usage but for two arrays of data: current position object and predicted (do not confuse 'predicted' with 'filtered').