Particle Filter Tracking in Python

Particle Filter Tracking in Python

• 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