Day #279- Multi-Object Tracking

Project #1 - vehicle-speed-check

Clone Repository - Link

On Anaconda prompt,
cd vehicle-speed-check
pip install -r requirements.txt
python speed_check.py

Comments - Very good project to get started. The logic of speed computation with respect to frames per second, pixel movement can be reused in other use cases. Use of dlib correlation tracker. The tracking logic can be reused in other similar implementation

Project #2 - Simple Example code (ROI Based)

	#pip uninstall opencv-python
	#pip uninstall opencv-contrib-python
	#pip intall opencv-contrib-python.
	#Learnt from https://www.learnopencv.com/multitracker-multiple-object-tracking-using-opencv-c-python/
	#Rewrote based on my requirements
	import cv2
	import sys
	# Read video
	video = cv2.VideoCapture(r"E:\Code_Repo\vehicle-speed-check\cars.mp4")
	#Create two trackers
	trackers = []
	tracker1 = cv2.TrackerKCF_create()
	trackers.append(tracker1)
	tracker2 = cv2.TrackerKCF_create()
	trackers.append(tracker2)
	tracker3 = cv2.TrackerKCF_create()
	trackers.append(tracker3)
	if not video.isOpened():
	print("Could not open video")
	sys.exit()
	# Read first frame.
	ok, frame1 = video.read()
	height , width , layers = frame1.shape
	new_h=int(height/2)
	new_w=int(width/2)
	frame = cv2.resize(frame1, (new_w, new_h))
	if not ok:
	print('Cannot read video file')
	sys.exit()
	bboxdata = []
	#Select two regions to track
	for i in range(3):
	bbox = cv2.selectROI(frame, False)
	bboxdata.append(bbox)
	print(bbox)
	print(bboxdata)
	# Initialize tracker with first frame and bounding box
	#tracker1 update
	ok1 = trackers[0].init(frame, bboxdata[0])
	#tracker2 update
	ok2 = trackers[1].init(frame, bboxdata[1])
	#tracker3 update
	ok3 = trackers[2].init(frame, bboxdata[2])
	flag = 0
	count = 0
	while True:
	# Read a new frame
	ok1, frame1 = video.read()
	height , width , layers = frame1.shape
	new_h=int(height/2)
	new_w=int(width/2)
	frame = cv2.resize(frame1, (new_w, new_h))
	if not ok1:
	break
	i = 0
	for tracker in trackers:
	result, bbox = tracker.update(frame)
	i = i + 1
	if result:
	# Tracking success
	p1 = (int(bbox[0]), int(bbox[1]))
	p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
	cv2.rectangle(frame, p1, p2, (255,0,0), 2, 1)
	else:
	print('Tracking failure for Object ', i)
	#remove from the list
	trackers.remove(tracker)
	#for every object that enters append to this
	if flag==0:
	bbox = cv2.selectROI(frame, False)
	tracker = cv2.TrackerKCF_create()
	tracker.init(frame, bbox)
	trackers.append(tracker)
	flag = 1
	cv2.imshow("Tracking", frame)
	# Exit if ESC pressed
	k = cv2.waitKey(1) & 0xff
	if k == 27: break
	cv2.destroyAllWindows()
	print(trackers)

view raw OpenCV_Custom_Tracker.py hosted with ❤ by GitHub

Project #3 - Another interesting project from Adrian blog 

Cloned the project and executed the demo. This code does not work in windows 10 though. Someone has fixed the code. The working code is in link 

python multi_object_tracking_fast.py --prototxt E:\Code_Repo\multiobject-tracking-dlib\mobilenet_ssd\MobileNetSSD_deploy.prototxt  --model E:\Code_Repo\multiobject-tracking-dlib\mobilenet_ssd\MobileNetSSD_deploy.caffemodel --video E:\Code_Repo\multiobject-tracking-dlib\race.mp4 --output E:\Code_Repo\multiobject-tracking-dlib\race_output_fast.avi

More Reads
Speed Detection

Multiple object tracking using dlib library, Python

Face Tracking in Python using Xailient Face Detector and dlib, Link

Object Tracker - Link

Happy Learning!!!