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.