Object tracking basics in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to initialize the OpenCV tracker.

Computer Vision

tracker = cv2.TrackerCSRT_create()  # Create CSRT tracker
success, frame = video.read()
bbox = cv2.selectROI(frame, False)
tracker = cv2.TrackerCSRT_create()
tracker.init(frame, [1])

Drag options to blanks, or click blank then click option'

Avideo

Bframe

Cbbox

Dsuccess

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the whole frame instead of the bounding box to tracker.init()

Using video or success variables instead of bbox

The tracker needs the bounding box (bbox) to know what object to track in the initial frame.

2fill in blank

medium

Complete the code to update the tracker and get the new bounding box.

Computer Vision

success, frame = video.read()
success, [1] = tracker.update(frame)
if 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)

Drag options to blanks, or click blank then click option'

Aframe

Bbbox

Csuccess

Dvideo

Attempts:

3 left

💡 Hint

Common Mistakes

Using frame instead of bbox to store the updated location

Confusing success with bbox in the assignment

The tracker.update() returns success and the new bounding box (bbox) for the tracked object.

3fill in blank

hard

Fix the error in the code to correctly select the ROI for tracking.

Computer Vision

frame = video.read()[1]
bbox = cv2.selectROI([1], False)

Drag options to blanks, or click blank then click option'

Asuccess

Bvideo

Ctracker

Dframe

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the video capture object instead of the frame image

Passing the tracker object instead of the frame

cv2.selectROI requires the current frame image to select the region of interest (ROI).

4fill in blank

hard

Fill both blanks to create a dictionary of trackers and update each one.

Computer Vision

trackers = {}
trackers['car'] = cv2.TrackerKCF_create()
trackers['car'].init(frame, bbox_car)
trackers['person'] = cv2.TrackerMIL_create()
trackers['person'].init(frame, bbox_person)

for name, tracker in trackers.items():
    success, [1] = tracker.update(frame)
    if success:
        p1 = (int([2][0]), int([2][1]))

Drag options to blanks, or click blank then click option'

Abbox

Bbbox_car

Cbbox_person

Dframe

Attempts:

3 left

💡 Hint

Common Mistakes

Using bbox_car or bbox_person inside the loop instead of the updated bbox

Using frame instead of bbox for the bounding box variable

The update returns the new bounding box which should be stored in bbox to draw the rectangle.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that tracks objects with bounding boxes larger than 5000 pixels.

Computer Vision

large_objects = {name: bbox for name, bbox in object_bboxes.items() if ([1] * [2]) [3] 5000}

Drag options to blanks, or click blank then click option'

Abbox[2]

Bbbox[3]

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using x or y instead of width and height for area calculation

Using less than instead of greater than for filtering

The width (bbox[2]) times height (bbox[3]) gives the area. We filter for area greater than 5000.

Practice

1. What is the main goal of object tracking in computer vision?

easy

A. To follow the position of an object across video frames

B. To classify objects into categories

C. To enhance the resolution of video frames

D. To detect edges in a single image

Solution

1. Step 1: Understand object tracking purpose

   Object tracking aims to keep track of where an object moves in a video over time.

2. Step 2: Differentiate from other tasks

   Classification sorts objects into groups, enhancement improves image quality, and edge detection finds boundaries in one image, not tracking movement.

3. Final Answer:

   To follow the position of an object across video frames -> Option A

4. Quick Check:

   Object tracking = follow object position [OK]

Hint: Tracking means following object movement frame by frame [OK]

Common Mistakes:

• Confusing tracking with classification
• Thinking tracking enhances image quality
• Mixing tracking with edge detection

2. Which of the following is the correct way to initialize an OpenCV tracker in Python?

easy

A. tracker = cv2.TrackerKCF()

B. tracker = cv2.Tracker_create('KCF')

C. tracker = cv2.createTracker('KCF')

D. tracker = cv2.TrackerKCF_create()

Solution

1. Step 1: Recall OpenCV tracker initialization syntax

   In OpenCV, trackers like KCF are created using the method cv2.TrackerKCF_create().

2. Step 2: Check each option

   tracker = cv2.Tracker_create('KCF') uses a non-existent method, C uses wrong method name, D tries to instantiate class directly which is incorrect.

3. Final Answer:

   tracker = cv2.TrackerKCF_create() -> Option D

4. Quick Check:

   OpenCV tracker init = TrackerKCF_create() [OK]

Hint: OpenCV trackers use create() methods, not constructors [OK]

Common Mistakes:

• Using wrong method names
• Trying to instantiate tracker classes directly
• Confusing createTracker with Tracker_create

3. Given the following Python code snippet using OpenCV tracker, what will be printed?

import cv2
tracker = cv2.TrackerKCF_create()
frame = cv2.imread('frame1.jpg')
bbox = (100, 100, 50, 50)
tracker.init(frame, bbox)
success, box = tracker.update(frame)
print(success, box)

medium

A. True and the original bbox tuple

B. True and a tuple with updated bounding box coordinates

C. SyntaxError due to wrong method names

D. False and None

Solution

1. Step 1: Understand tracker initialization and update

   The tracker is initialized with a frame and bbox, then updated on the same frame.

2. Step 2: Predict update output on same frame

   Since the frame hasn't changed, the tracker will succeed and return the updated bbox coordinates (which may be the same as original but returned as a new tuple).

3. Final Answer:

   True and a tuple with updated bounding box coordinates -> Option B

4. Quick Check:

   Update on same frame = success True, bbox updated [OK]

Hint: Update on same frame returns success True and updated box [OK]

Common Mistakes:

• Expecting bbox to change without new frame
• Confusing success flag meaning
• Assuming syntax errors in correct code

4. Identify the error in this object tracking code snippet:

import cv2
tracker = cv2.TrackerKCF_create()
frame = cv2.imread('frame1.jpg')
bbox = (100, 100, 50, 50)
tracker.init(frame, bbox)
success, box = tracker.update()
print(success, box)

medium

A. Missing frame argument in tracker.update() call

B. bbox should be a list, not a tuple

C. tracker.init() should be called after update()

D. cv2.imread() returns None if file not found

Solution

1. Step 1: Check tracker.update() method signature

   The update() method requires the current frame as an argument to track the object.

2. Step 2: Identify missing argument

   The code calls update() without any argument, causing an error.

3. Final Answer:

   Missing frame argument in tracker.update() call -> Option A

4. Quick Check:

   update() needs frame argument [OK]

Hint: update() always needs the current frame as input [OK]

Common Mistakes:

• Forgetting frame argument in update()
• Thinking bbox must be list not tuple
• Calling init() after update()

5. You want to track a fast-moving object in a video with limited computing power. Which tracker should you choose for a good balance of speed and accuracy?

hard

A. MIL tracker

B. CSRT tracker

C. KCF tracker

D. BOOSTING tracker

Solution

1. Step 1: Understand tracker characteristics

   KCF tracker is known for good speed and decent accuracy, suitable for fast objects and limited resources.

2. Step 2: Compare with other trackers

   MIL and BOOSTING are slower or less accurate; CSRT is more accurate but slower and heavier on computation.

3. Final Answer:

   KCF tracker -> Option C

4. Quick Check:

   Fast object + limited power = KCF tracker [OK]

Hint: KCF balances speed and accuracy well for fast objects [OK]

Common Mistakes:

• Choosing CSRT for speed instead of accuracy
• Assuming BOOSTING is fastest
• Ignoring resource limits