Object tracking basics in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main goal of object tracking in video?

Imagine you are watching a soccer game video. What is the main goal of object tracking?

A To classify the type of sport being played in the video

B To identify and follow the position of a specific player across all video frames

C To detect all players only in the first frame

D To enhance the video quality by increasing resolution

💡 Hint

Think about what it means to keep track of something moving in a video.

Object tracking means locating the same object in every frame of a video, like following a player as they move.

❓ Predict Output

intermediate

What does this simple tracking code output?

Consider this Python code snippet that simulates tracking a moving object by updating its position:

Computer Vision

positions = [5, 10, 15]
tracked_positions = []
for pos in positions:
    tracked_positions.append(pos + 2)
print(tracked_positions)

A SyntaxError

B [5, 10, 15]

C [2, 7, 12]

D [7, 12, 17]

💡 Hint

Look at how each position is changed before adding to the list.

The code adds 2 to each position before storing it, so the output list has each original value plus 2.

❓ Model Choice

advanced

Which model is best suited for real-time object tracking in videos?

You want to track objects in a live video feed with minimal delay. Which model type is best?

A A model combining convolutional neural networks with recurrent layers to use past frames

B A model that only detects objects in the first frame and ignores the rest

C A deep learning model that processes each frame independently without temporal info

D A model that uses only traditional image filters without learning

💡 Hint

Think about how using information from previous frames helps tracking.

Models combining CNNs with recurrent layers (like LSTM) can remember past frames, improving tracking accuracy and speed.

❓ Metrics

advanced

Which metric best measures tracking accuracy over time?

When evaluating an object tracking system, which metric tells you how well the tracker follows the object across frames?

A Mean squared error between predicted and true object size only

B Classification accuracy on a single frame

C Intersection over Union (IoU) averaged over all frames

D Number of detected objects in the first frame

💡 Hint

Think about a metric that compares predicted and actual object positions over time.

IoU averaged over frames measures how well the predicted bounding boxes overlap with the true boxes throughout the video.

🔧 Debug

expert

Why does this tracking code fail to update the object's position correctly?

Review this Python code snippet for tracking an object position. Why does it not update the position as expected?

Computer Vision

position = 0
for frame in range(3):
    position = position
    position += frame
print(position)

A The line 'position = position' is redundant but does not cause failure; the code outputs 3

B The code raises a SyntaxError due to incorrect assignment

C The position variable is never updated because 'position += frame' is inside a comment

D The loop does not run because range(3) is empty

💡 Hint

Check if the code runs and what the final value of position is after the loop.

The line 'position = position' does nothing but does not stop the update. The position increases by 0+1+2 = 3.

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