We need an object detector to detect the object so we can use the position in object tracking.
We use a pre-trained Yolov3 on MS coco to save time. You can select your own detector, just get the position.
Be sure to restart the runtime if instructed
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If all links below expired, please download the pre-trained model from imageai's official website: https://imageai.readthedocs.io/en/latest/detection/index.html
!wget https://dl.acytoo.com/yolo.h5 # if this site is down, use https://play.acytoo.com/yolo.h5 instead
--2022-04-23 06:15:26-- https://dl.acytoo.com/yolo.h5 Resolving dl.acytoo.com (dl.acytoo.com)... 129.158.55.57 Connecting to dl.acytoo.com (dl.acytoo.com)|129.158.55.57|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 248686624 (237M) [application/x-hdf] Saving to: ‘yolo.h5’ yolo.h5 100%[===================>] 237.17M 123MB/s in 1.9s 2022-04-23 06:15:28 (123 MB/s) - ‘yolo.h5’ saved [248686624/248686624]
Define a general detector model: yolov3, trained on ms coco dataset.
Yolo(You Only Look Once) is one stage concolutional neural network for peforming object detection in real time. Compared to two stage CNNs, Yolo is faster and consumes less computing resources, but it's precision is lower than, it's a Speed-Accuracy Trade-Off.
Imageai is a python module that has many pre-trained object detection model.
For detailed information about imageai, please refer to https://imageai.readthedocs.io/en/latest/detection/index.html
from imageai.Detection import ObjectDetection
detector = ObjectDetection()
detector.setModelTypeAsYOLOv3()
detector.setModelPath("yolo.h5")
detector.loadModel()
Yolo trained on ms coco can detect more than 170 kinds of object, we only need ball here, so after perform yolo on the input image, we select the objects that detedted as ball.
Ball detector, input is image, return probility and location
def ball_detector(img, threshold=30):
"""
:param img: image matrix
:param threshold: min probability that one object is detected as ball
:return: [probability], [location]
"""
img_ori_dec, detections = detector.detectObjectsFromImage(input_image=img, input_type = "array",
output_type="array", minimum_percentage_probability=threshold)
prob = []
loc = []
for item in detections:
if item['name'] == 'sports ball':
prob.append(item['percentage_probability'])
loc.append(item['box_points'])
return prob, loc
%matplotlib inline
import matplotlib.pyplot as plt
import cv2
!wget https://dl.acytoo.com/ball.mp4
!wget https://dl.acytoo.com/multiObject.avi
--2022-04-23 06:16:01-- https://dl.acytoo.com/ball.mp4 Resolving dl.acytoo.com (dl.acytoo.com)... 129.158.55.57 Connecting to dl.acytoo.com (dl.acytoo.com)|129.158.55.57|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 1368142 (1.3M) [video/mp4] Saving to: ‘ball.mp4’ ball.mp4 100%[===================>] 1.30M --.-KB/s in 0.08s 2022-04-23 06:16:01 (15.4 MB/s) - ‘ball.mp4’ saved [1368142/1368142] --2022-04-23 06:16:01-- https://dl.acytoo.com/multiObject.avi Resolving dl.acytoo.com (dl.acytoo.com)... 129.158.55.57 Connecting to dl.acytoo.com (dl.acytoo.com)|129.158.55.57|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 1769608 (1.7M) [video/x-msvideo] Saving to: ‘multiObject.avi’ multiObject.avi 100%[===================>] 1.69M --.-KB/s in 0.09s 2022-04-23 06:16:01 (19.7 MB/s) - ‘multiObject.avi’ saved [1769608/1769608]
In above detector, our detector result is denoted as left top(x1, y1) and right bottom(x2, y2) vertexes, in order to apply kalman filter, we need to find the centroid of object(the ball). The centroid of ball can be calculated as $$ x_c = x_1 + \frac{(x_2 - x_1)}{2}\\ y_c = y_2 + \frac{(y_1 - y_2)}{2}\\ $$
def get_centroid(loc):
if loc is None:
return None
return int(loc[0] + (loc[2] - loc[0]) / 2), int(loc[3] + (loc[1] - loc[3]) / 2)
import numpy as np
from scipy.optimize import linear_sum_assignment # to assign detectors to objects
class KalmanFilter():
def __init__(self, dt=0.1, u_x=1, u_y=1,
std_acc=1, x_std_meas=0.1, y_std_meas=0.1):
"""
:param dt: sampling time (time for 1 cycle)
:param u_x: acceleration in x-direction
:param u_y: acceleration in y-direction
:param std_acc: process noise magnitude
:param x_std_meas: standard deviation of the measurement in x-direction
:param y_std_meas: standard deviation of the measurement in y-direction
"""
self.dt = dt # Define sampling time
self.u = np.matrix([[u_x],[u_y]]) # Define the control input variables
self.x = np.matrix([[0], [0], [0], [0]]) # Initial State
# Define the State Transition Matrix A
self.A = np.matrix([[1, 0, self.dt, 0],
[0, 1, 0, self.dt],
[0, 0, 1, 0],
[0, 0, 0, 1]])
# Define the Control Input Matrix B
self.B = np.matrix([[(self.dt**2)/2, 0],
[0,(self.dt**2)/2],
[self.dt,0],
[0,self.dt]])
# Define Measurement Mapping Matrix
self.H = np.matrix([[1, 0, 0, 0],
[0, 1, 0, 0]])
#Initial Process Noise Covariance
self.Q = np.matrix([[(self.dt**4)/4, 0, (self.dt**3)/2, 0],
[0, (self.dt**4)/4, 0, (self.dt**3)/2],
[(self.dt**3)/2, 0, self.dt**2, 0],
[0, (self.dt**3)/2, 0, self.dt**2]]) * std_acc**2
#Initial Measurement Noise Covariance
self.R = np.matrix([[x_std_meas**2,0],
[0, y_std_meas**2]])
#Initial Covariance Matrix
self.P = np.eye(self.A.shape[1])
def predict(self):
"""
:return: numpy matrix
"""
# Update time state
self.x = np.dot(self.A, self.x) + np.dot(self.B, self.u)
# Calculate error covariance
self.P = np.dot(np.dot(self.A, self.P), self.A.T) + self.Q
return self.x[0:2]
def update(self, z):
"""
:param z: np.array
:return: numpy matrix
"""
# S = H*P*H'+R
S = np.dot(self.H, np.dot(self.P, self.H.T)) + self.R
# Calculate the Kalman Gain
# K = P * H'* inv(H*P*H'+R)
K = np.dot(np.dot(self.P, self.H.T), np.linalg.inv(S))
self.x = np.round(self.x + np.dot(K, (z - np.dot(self.H, self.x))))
I = np.eye(self.H.shape[1])
# Update error covariance matrix
self.P = (I - (K * self.H)) * self.P
return self.x[0:2]
Green square is measurement, and red dot is prediction
def predict_ball(src, dest):
"""
:param src: source video for detection and predicton
:param dest: output video after processing
detection: gree square box
prediction: red dot
"""
COLOR_RED = (255, 0, 0)
COLOR_GREEN = (0, 255, 0)
############################ init kalman filter ##############################
kalman = KalmanFilter()
# set first point, the first location is VERY important
kalman.update(np.array([[906], [311]], dtype=np.float32))
vid = cv2.VideoCapture(src)
if not vid.isOpened():
print("File open error, please download video")
############################ params for saving video #########################
fps = int(vid.get(cv2.CAP_PROP_FPS)) # fps
height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)) # height
width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)) # width
size = (width, height)
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
out = cv2.VideoWriter(dest, fourcc, fps, size)
current_measurement = None
current_prediction = (905, 311)
while vid.isOpened(): # read all video frames
ret, frame = vid.read()
if not ret:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # convert bgr to rgb
_, loc = ball_detector(frame)
lpx, lpy = int(current_prediction[0]), int(current_prediction[1])
frame = cv2.circle(frame, (lpx, lpy), 0, COLOR_RED, 20) # plot prediction dot
if len(loc) > 0: # detection -> draw bounding box, update kalman filter
frame = cv2.rectangle(frame, (loc[0][0], loc[0][1]), (loc[0][2], loc[0][3]), COLOR_GREEN, 2)
# update kalman
current_measurement = np.array(get_centroid(loc[0]), dtype=np.float32).reshape(2, 1)
kalman.update(current_measurement)
(x, y) = kalman.predict()
current_prediction = (int(x), int(y))
plt.imshow(frame, interpolation='nearest')
plt.show()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
out.write(frame)
vid.release() # release video object
out.release()
sin = 'ball.mp4'
sin_out = 'sin_track.avi'
print("processing sin")
predict_ball(sin, sin_out)
processing sin
Multiple object tracking(MOT) is much more difficult than single object tracking, what if the two objects crossing each other? What if two objects collide and bound the other direction? What if a new object just show up and another disappear?
We can use a data structure to store all the information about one object, let's call this data structure tracker. The number of tracker is one more than the number of object, when one object disappear for some consecutive frames, delete one tracker, a new objest shows up, add one tracker. To match trackers to objects, we use a loss function, calculate the error between detection and prediction.
The linear sum assignment problem is also known as minimum weight matching in bipartite graphs. A problem instance is described by a matrix C, where each C[i,j] is the cost of matching vertex i of the first partite set (a “worker”) and vertex j of the second set (a “job”). The goal is to find a complete assignment of workers to jobs of minimal cost.
Details of the method used here to assign tracker to object: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html
from dataclasses import dataclass
@dataclass
class Tracker:
id: int
centroid: tuple
kalman: KalmanFilter()
consecutive_invi_num = 0
Loss: Euclidean distance
def distance(predict, centroid):
"""
Params: np.array
Return: float
Euclidean distance
"""
return np.linalg.norm(predict - centroid)
cost_of_non_assigned = 500
def assign_tracks(costs):
row_ind, col_ind = linear_sum_assignment(costs)
return row_ind, col_ind
def predict_multiple_ball(src, dest):
"""
src: source video for detection and predicton
dest: output video after processing
detection: gree square box
prediction: red dot
"""
colors_dot = [(0, 255, 0), (255, 0, 0), (255, 255, 0), (0, 0, 255)] # different color for different object
vid = cv2.VideoCapture(src)
if not vid.isOpened():
print("File open error, please download video")
############################ params for saving video #########################
fps = int(vid.get(cv2.CAP_PROP_FPS)) # fps
height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)) # height
width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)) # width
size = (width, height)
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
out = cv2.VideoWriter(dest, fourcc, fps, size)
font = cv2.FONT_HERSHEY_SIMPLEX
trackers = []
tracker = Tracker(id=0, centroid=(621, 172), kalman=KalmanFilter(u_x=-0.5, u_y=0))
tmp = np.array(tracker.centroid, dtype=np.float32).reshape(2, 1)
tracker.kalman.update(tmp)
trackers.append(tracker)
while vid.isOpened(): # read all video frames
# read frame, convert to rgb
ret, frame = vid.read()
if not ret:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # convert bgr to rgb
# detect the object in image
_, loc = ball_detector(frame) # return ([probs], [locs])
detection_num = len(loc)
cens = []
for l in loc:
# frame = cv2.rectangle(frame, (l[0], l[1]), (l[2], l[3]), colors_box[1], 2) # show detector box
cens.append(get_centroid(l)) # get centroid of all detection
# If not enough tracker, add new tracker for new object
if detection_num > len(trackers):
tracker = Tracker(id=1, centroid=(27, 149), kalman=KalmanFilter(u_x=0.5, u_y=0)) # the initial location is VERY important
tracker.kalman.update(np.array(tracker.centroid, dtype=np.float32).reshape(2, 1))
trackers.append(tracker)
# predict new location for all existing tracker
for tracker in trackers:
(cx, cy) = tracker.kalman.predict()
tracker.centroid = (int(cx), int(cy))
# Tracker management
# calculate lose
costs = []
for tracker in trackers:
current_costs = []
for c in cens:
cen = np.array(c, dtype=np.float32)
current_costs.append(distance(tracker.centroid, cen)) # prediction and detection
costs.append(current_costs)
# assign tracker to object, update kalman filter, set invisible number to 0
tracker_id, obj_id = assign_tracks(np.array(costs, dtype=np.float32))
# obj_id = np.flip(obj_id)
for tid, oid in zip(tracker_id, obj_id):
measure_cen = np.array(cens[oid]).reshape(2, 1)
trackers[tid].kalman.update(measure_cen)
# print('predict', trackers[tid].centroid)
# print('detected', cens[oid])
# show prediction
for tracker in trackers:
cx = int(tracker.centroid[0])
cy = int(tracker.centroid[1])
frame = cv2.circle(frame, (cx, cy), 0, colors_dot[tracker.id], 20)
frame = cv2.putText(frame, f'{tracker.id}', (cx, cy - 15), font, 1, colors_dot[tracker.id], 2)
plt.imshow(frame, interpolation='nearest')
plt.show()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
out.write(frame)
vid.release() # release video object
out.release()
mul = 'multiObject.avi'
mul_out = 'mul_track.avi'
print("processing mul")
predict_multiple_ball(mul, mul_out)
processing mul