about matching detections and trackers
3073 opened this issue · comments
hi helper ... can I take the principle you used for matching detection and trackers and use dlib for creating a new tracker for the unmatched detection?
Sorry for the late reply. It should be the same principle
Thank you as always... normally the detection will run every 30 frames (
some frames will be skipped) for increasing the speed of processing because
the detection is slow. anyways i will provide you the result soon for
asking a help ...
…On Sun, Mar 17, 2019 at 6:48 PM Kyle Guan ***@***.***> wrote:
Sorry for the late reply. It should be the same principle
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Cool!
##### there are some code here not necessary for this particular
discussion.
def iou(self,a, b): ### I used your code directly here
w_intsec = np.maximum (0, (np.minimum(a[2], b[2]) -np.maximum(a[0], b[0])))
h_intsec = np.maximum (0, (np.minimum(a[3], b[3]) -np.maximum(a[1], b[1])))
s_intsec = w_intsec * h_intsec
s_a = (a[2] - a[0])*(a[3] - a[1])
s_b = (b[2] - b[0])*(b[3] - b[1])
return float(s_intsec)/(s_a + s_b -s_intsec)
def assign_detections_to_trackers(self ,trackers, detections, iou_thrd= 0.6): ### I used your code here
unmatched_trackers, unmatched_detections = [], []
IOU_mat= np.zeros((len(trackers),len(detections)),dtype=np.float32)
for t,trk in enumerate(trackers):
for d,det in enumerate(detections):
IOU_mat[t,d] = self.iou(trk,det)
matched_idx = linear_assignment(-IOU_mat)
unmatched_trackers, unmatched_detections = [], []
unmatched_trackers_box, unmatched_detections_box = [], []
for t,trk in enumerate(trackers):
if(t not in matched_idx[:,0]):
unmatched_trackers.append(t)
unmatched_trackers_box.append(trk)
for d, det in enumerate(detections):
if(d not in matched_idx[:,1]):
unmatched_detections.append(d)
self.unmatched_detections_box.append(det)
matches = []
for m in matched_idx:
if(IOU_mat[m[0],m[1]]<iou_thrd):
unmatched_trackers.append(m[0])
unmatched_detections.append(m[1])
else:
matches.append(m.reshape(1,2))
if(len(matches)==0):
matches = np.empty((0,2),dtype=int)
else:
matches = np.concatenate(matches,axis=0)
print(matches, unmatched_detections, unmatched_trackers)
return matches, np.array(unmatched_detections), np.array(unmatched_trackers)
def people(self, frame): #### start to read the code here
self.rects = []
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if self.totalFrames % 20 == 0 : ### every 20 frame there will be detection
points = self.det.get_points(frame) #### detection model will be executed
assignation = self.assign_detections_to_trackers(self.prev_points,points,iou_thrd = 0.6)
if len(assignation[0]) > 0: #### updating the tracked objects previously if a match is found
tracker_list =[]
for trk_idx,det_idx in assignation[0]:
tmp_trk = self.trackers[trk_idx]
tracker_list.append(tmp_trk)
self.update_tracker(tracker_list,frame)
if len(assignation[2]) > 0: #### delete the tracker if there is unmatched tracker
indexes = []
for trk_idx in assignation[2]:
indexes.append(trk_idx)
for index in sorted(indexes, reverse=True):
del self.trackers[index]
if len(assignation[1])> 0: ### generate new tracker for the new object detected
dete_points = []
for idx in assignation[1]:
point = points[idx]
dete_points.append(point)
self.generate_tracker(frame,dete_points)
else: #### if the frame number is not divisible by 20 updating the trackers will continue
self.update_tracker(self.trackers,frame)
self.prev_points = self.rects ### previous updated tackers will be noted for calculating the overlap
people = self.finder.update(self.rects)
##HERE GOES ANOTHER CODE !!!
return frame
def update_tracker(self,trackers,frame):
for tracker in trackers:
tracker.update(frame)
pos = tracker.get_position()
startX = int(pos.left())
startY = int(pos.top())
endX = int(pos.right())
endY = int(pos.bottom())
rect = (startX, startY, endX, endY)
self.rects.append(rect)
cv2.rectangle(frame, (startX, startY), (endX, endY), (250,250,250), 4)
return self.rects
def generate_tracker(self,frame,detections_box):
for dete_point in detections_box:
(startX, startY, endX, endY) = dete_point
tracker = dlib.correlation_tracker()
rect = dlib.rectangle(int(startX), int(startY), int(endX), int(endY))
tracker.start_track(frame, rect)
self.trackers.append(tracker)
pos = tracker.get_position()
startX = int(pos.left())
startY = int(pos.top())
endX = int(pos.right())
endY = int(pos.bottom())
rect = (startX, startY, endX, endY)
self.rects.append(rect)
cv2.rectangle(frame, (startX, startY), (endX, endY), (0,0,250), 4)
return self.rects
#### this code works fine but i need some help....
###### Questions to discuss on
how do we select the best frame skipped to make it a general purpose
code ? for instance for this particular video the best result is obtained
when the skipped frame is 20.
since this is a counting code a person that did not detected at the
detection frame number for example 20 will not be tracked for the next 20
frames ( up to frame number 40). so if the person get lost after the 40
frame , it will not be counted at all and that is a counting error..... do
you have a fix to this?
will great and honest thanks
@3073 , sorry for the late reply. This seems to me a very difficult problem to solve, honestly. I have been struggling with finding parameters that work well with a wide range of scenarios.
thank you....
ok sir
…On Sun, Mar 24, 2019 at 6:47 PM Kyle Guan ***@***.***> wrote:
@3073 <https://github.com/3073> , keep me posted if you find a solution.
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