--- a/python/moving.py	Wed Jun 14 01:11:56 2017 -0400
+++ b/python/moving.py	Wed Jun 21 17:21:03 2017 -0400
@@ -1590,7 +1590,7 @@
         else:
             self.userTypes[instant] = userType2Num['unknown']
 
-    def classifyUserTypeHoGSVM(self, pedBikeCarSVM = None, width = 0, height = 0, homography = None, images = None, bikeCarSVM = None, pedBikeSpeedTreshold = float('Inf'), bikeCarSpeedThreshold = float('Inf'), minSpeedEquiprobable = -1, speedProbabilities = None, aggregationFunc = median, nInstantsIgnoredAtEnds = 0, px = 0.2, py = 0.2, minNPixels = 800, rescaleSize = (64, 64), orientations = 9, pixelsPerCell = (8,8), cellsPerBlock = (2,2)):
+    def classifyUserTypeHoGSVM(self, pedBikeCarSVM = None, width = 0, height = 0, homography = None, images = None, bikeCarSVM = None, pedBikeSpeedTreshold = float('Inf'), bikeCarSpeedThreshold = float('Inf'), minSpeedEquiprobable = -1, speedProbabilities = None, aggregationFunc = median, maxPercentUnknown = 0.5, nInstantsIgnoredAtEnds = 0, px = 0.2, py = 0.2, minNPixels = 800, rescaleSize = (64, 64), orientations = 9, pixelsPerCell = (8,8), cellsPerBlock = (2,2)):
         '''Agregates SVM detections in each image and returns probability
         (proportion of instants with classification in each category)
 
@@ -1611,14 +1611,20 @@
             userTypeProbabilities = {userType2Num['car']: 1., userType2Num['pedestrian']: 1., userType2Num['bicycle']: 1.}
         else:
             userTypeProbabilities = {userType2Num[userTypename]: speedProbabilities[userTypename](self.aggregatedSpeed) for userTypename in speedProbabilities}
-        # result is P(Class|Appearance) x P(Speed|Class)
+        # compute P(Class|Appearance)
         nInstantsUserType = {userTypeNum: 0 for userTypeNum in userTypeProbabilities}# number of instants the object is classified as userTypename
+        nInstantsUserType[userType2Num['unknown']] = 0
         for t in self.userTypes:
-            nInstantsUserType[self.userTypes[t]] = nInstantsUserType.get(self.userTypes[t], 0) + 1
-        for userTypeNum in userTypeProbabilities:
-            userTypeProbabilities[userTypeNum] *= nInstantsUserType[userTypeNum]
+            nInstantsUserType[self.userTypes[t]] += 1 #nInstantsUserType.get(self.userTypes[t], 0) + 1
+        # result is P(Class|Appearance) x P(Speed|Class)
+        if nInstantsUserType[userType2Num['unknown']] < maxPercentUnknown*self.length(): # if not too many unknowns
+            for userTypeNum in userTypeProbabilities:
+                userTypeProbabilities[userTypeNum] *= nInstantsUserType[userTypeNum]
         # class is the user type that maximizes usertype probabilities
-        self.setUserType(utils.argmaxDict(userTypeProbabilities))
+        if nInstantsUserType[userType2Num['unknown']] >= maxPercentUnknown*self.length() and (speedProbabilities is None or self.aggregatedSpeed < minSpeedEquiprobable): # if no speed information and too many unknowns
+            self.setUserType(userType2Num['unknown'])
+        else:
+            self.setUserType(utils.argmaxDict(userTypeProbabilities))
 
     def classifyUserTypeArea(self, areas, homography):
         '''Classifies the object based on its location (projected to image space)