class ReIDTracker(Tracker):
"""
Specialized tracker class which inherits from the basic Tracker class
Utilizes the KalmanFilter and a person re-identification neural network for more accurate bounding box associations
"""
def __init__(self, matching_threshold=0.2, diff_threshold=0.2):
""" Initialize the tracker from base class with relevant metrics """
Tracker.__init__(self, matching_threshold=matching_threshold)
self.metric_nn = Metric('ReIDNN')
self.metric_iou = Metric('iou')
self.diff_threshold = diff_threshold
def update(self, frame, detections):
"""
Core method of the tracker, updates tracked objects after receiving detections.
Associates detection with existing tracked objects, deletes disappeared detection and creates
new trackers if non are associated with detections.
:param frame: (array) np.ndarray representing image frame associated with current detections
:param detections: (array) list of detections (bounding boxes in [x1,x2,y1,y2] format)
:return (array) list of tracked objects tuples in format (ID, [x1,x2,y1,y2])
"""
# Check to see if there are no detections and handle if so
if len(detections) == 0:
return self.handle_no_detections()
# if we are currently not tracking any objects, register all detections to new tracks
if len(self.tracked) == 0:
for i in range(0, len(detections)):
self.register(detections[i])
# otherwise, check to see how the new detections relate to current tracks
else:
self.associate(frame, detections)
# return the corrected estimate of tracked objects and their unique ids
return self.project()
def associate(self, frame, detections):
"""
Performs the Hungarian algorithm and assignment between detections and existing
trackers according to distance matrix and metric specified.
updates the Tracker class dictionaries after associations
:param frame: (array) np.ndarray representing image frame associated with current detections
:param detections: (array) list of new detections
"""
# Grab the set of object IDs and corresponding states
track_ids = list(self.tracked.keys())
# Get predicted tracked object states from Kalman Filter
tracked_states = [track.predict() for track in self.tracked.values()]
# Crop the image from each bounding box associated to new detections and current tracks
tracked_crops = Tracker.crop_bbox_from_frame(frame, tracked_states)
detections_crops = Tracker.crop_bbox_from_frame(frame, detections)
# Compute the distance matrix between detections and trackers according to metric
D = self.metric_iou.distance_matrix(tracked_states, detections)
D_iou_sorted = -np.sort(-D)
# Check if there are difficult overlapping IoU between a track and several detections
for row_idx in xrange(D.shape[0]):
if (D_iou_sorted[row_idx, 0] - D_iou_sorted[row_idx, 1] <
self.diff_threshold) and (D_iou_sorted[row_idx, 0] >
self.matching_threshold):
# Consult with re-identification network
D_nn = self.metric_nn.distance_matrix(tracked_crops,
detections_crops)
D = np.multiply(D, D_nn)
break
# Associate detections to existing trackers according to distance matrix
self.linear_assignment(D, track_ids, detections)
class ORBTracker(Tracker):
"""
Specialized tracker class which inherits from the basic Tracker class
Utilizes the KalmanFilter and feature matching (ORB) for more accurate bounding box associations
"""
def __init__(self, matching_threshold=0.01):
""" Initialize the tracker from base class with relevant metrics """
Tracker.__init__(self, matching_threshold)
self.metric_orb = Metric('ORB')
self.metric_iou = Metric('iou')
def update(self, frame, detections):
"""
Core method of the tracker, updates tracked objects after receiving detections.
Associates detection with existing tracked objects, deletes disappeared detection and creates
new trackers if non are associated with detections.
:param frame: (array) np.ndarray representing image frame associated with current detections
:param detections: (array) list of detections (bounding boxes in [x1,x2,y1,y2] format)
:return (array) list of tracked objects tuples in format (ID, [x1,x2,y1,y2])
"""
# Check to see if there are no detections and handle if so
if len(detections) == 0:
return self.handle_no_detections()
# if we are currently not tracking any objects, register all detections to new tracks
if len(self.tracked) == 0:
for i in range(0, len(detections)):
self.register(detections[i])
# otherwise, check to see how the new detections relate to current tracks
else:
self.associate(frame, detections)
# return the corrected estimate of tracked objects and their unique ids
return self.project()
def associate(self, frame, detections):
"""
Performs the Hungarian algorithm and assignment between detections and existing
trackers according to distance matrix and metric specified.
updates the Tracker class dictionaries after associations
:param frame: (array) np.ndarray representing image frame associated with current detections
:param detections: (array) list of new detections
"""
# Grab the set of object IDs and corresponding states
track_ids = list(self.tracked.keys())
# Get predicted tracked object states from Kalman Filter
tracked_states = [track.predict() for track in self.tracked.values()]
# Crop the image from each bounding box associated to new detections and current tracks
tracked_crops = Tracker.crop_bbox_from_frame(frame, tracked_states)
detections_crops = Tracker.crop_bbox_from_frame(frame, detections)
# Compute the distance matrix between detections and trackers according to metric
D_orb = self.metric_orb.distance_matrix(tracked_crops,
detections_crops)
D_iou = self.metric_iou.distance_matrix(tracked_states, detections)
w = 0.2
D = np.multiply(w * D_orb, (1 - w) * D_iou)
# Associate detections to existing trackers according to distance matrix
self.linear_assignment(D, track_ids, detections)
