1. Particle Filter Tracking
in Python
12 1 8

2. About myself
google it ➡ Kohta Ishikawa
(a professional skier is not me)
12 1 8

3. Particle
distribution empirical
approximation
12 1 8

4. Filter
movement estimation observation
and
Bayes update
12 1 8

5. Particle Filter
particle movement observation
and
resampling
12 1 8

6. Tracking
12 1 8

7. Tracking
12 1 8

8. Tracking
12 1 8

9. Tracking
12 1 8

10. Code
def filtration(svs,mv,systemModel,likelihood):
#
dim = len(svs[1])
N = len(svs)
sigma = 2.0
rnorm = stats.norm.rvs(0,sigma,size=N*dim)
ranges = zip([N*i for i in range(dim)],
[N*i for i in (range(dim+1)[1:])])
ws = np.array([rnorm[p:q] for p,q in ranges])
ws = ws.transpose()
#
svs_predict = [systemModel.generate(sv,w) for sv,w in zip(svs,ws)]
#
normalization_factor = likelihood.normalization(svs_predict,mv)
likelihood_weights = [likelihood.generate(sv,mv)/normalization_factor
for sv in svs_predict]
#
svs_resampled = resampling(svs_predict,likelihood_weights)
return(svs_resampled)
12 1 8

11. Code
if(__name__=="__main__"):
#
img = Image()
#
sampleSize = 100
#
systemModel = SystemModel(model_s)
likelihood = Likelihood(model_l)
#
svs = initStateVectors(img.size,sampleSize)
while(True):
#
showImage(svs,img)
#
img.create()
#
svs = filtration(svs,img,systemModel,likelihood)
12 1 8

12. Code (openCV Bundle)
class Image:
def __init__(self):
self.capture = cv.CreateCameraCapture(0)
self.image = cv.QueryFrame(self.capture)
cv.ShowImage("Capture",self.image)
self.size = (self.image.width,self.image.height)
def create(self):
self.image = cv.QueryFrame(self.capture)
def getCol(self,sv):
x = sv[0]
y = sv[1]
#
if((x<0 or x>self.size[0]) or (y<0 or y>self.size[1])):
return((0,0,0,0))
else:
return(cv.Get2D(self.image,int(sv[1]),int(sv[0])))
12 1 8

13. Demo
12 1 8

14. NumPy/SciPy
def model_s(sv,w):
#
# (x,y,vx,vy)
F = np.matrix([[1,0,1,0],
[0,1,0,1],
[0,0,1,0],
[0,0,0,1]])
return(np.array(np.dot(F,sv))[0]+w)
def filtration(svs,mv,systemModel,likelihood):
#
dim = len(svs[1])
N = len(svs)
sigma = 2.0
rnorm = stats.norm.rvs(0,sigma,size=N*dim)
ranges = zip([N*i for i in range(dim)],
[N*i for i in (range(dim+1)[1:])])
ws = np.array([rnorm[p:q] for p,q in ranges])
ws = ws.transpose()
12 1 8

15. References
photo: By Dale Gillard from everystockphoto.com
book:
code: http://d.hatena.ne.jp/koh_ta/20110814
https://github.com/kohta/pftrack
12 1 8