Real-time processing patterns in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding latency in real-time processing

In real-time computer vision systems, latency is a critical factor. Which of the following best describes the impact of high latency on a real-time video processing application?

A The system delays output, causing a noticeable lag between input and response.

B The system processes frames faster than they arrive, causing frame drops.

C The system improves accuracy by processing more frames per second.

D The system reduces power consumption by skipping frames.

💡 Hint

Think about what happens when processing takes too long compared to the input speed.

High latency means the system takes longer to process each frame than the time between frames arriving. This causes a delay or lag in the output, making the system less responsive.

❓ Model Choice

intermediate

Choosing a model for real-time object detection

You need to deploy an object detection model on a mobile device with limited computing power and require real-time performance. Which model architecture is most suitable?

A Mask R-CNN with ResNet-50 backbone

B Faster R-CNN with ResNet-101 backbone

C SSD with VGG16 backbone

D YOLOv4-tiny

💡 Hint

Look for a lightweight model optimized for speed on limited hardware.

YOLOv4-tiny is designed to be fast and lightweight, making it suitable for real-time detection on devices with limited resources.

❓ Hyperparameter

advanced

Adjusting batch size for real-time video processing

In a real-time video processing pipeline, you want to minimize latency while maintaining throughput. How does reducing the batch size during inference affect latency and throughput?

A Reducing batch size increases latency and increases throughput.

B Reducing batch size decreases latency but may reduce throughput.

C Reducing batch size increases latency and reduces throughput.

D Reducing batch size decreases latency and increases throughput.

💡 Hint

Think about how processing fewer frames at once affects speed and total frames processed per second.

Smaller batch sizes mean frames are processed faster individually, lowering latency, but the system may process fewer frames per second overall, reducing throughput.

❓ Metrics

advanced

Evaluating real-time system performance

You measure the following for a real-time video classification system: average frame processing time is 30 ms, and the input video runs at 25 frames per second (fps). What is the system's latency relative to the input frame rate?

A Latency is 25 ms, matching the frame interval, so the system is real-time.

B Latency is 30 ms, which is slower than the 40 ms frame interval, so the system cannot keep up.

C Latency is 30 ms, which is faster than the 40 ms frame interval, so the system can process in real-time.

D Latency is 40 ms, which is faster than the 30 ms frame interval, so the system can keep up.

💡 Hint

Calculate the time between frames from fps and compare to processing time.

At 25 fps, each frame arrives every 40 ms (1000 ms / 25). Since processing takes 30 ms per frame, the system processes frames faster than they arrive, enabling real-time operation.

🔧 Debug

expert

Identifying bottleneck in real-time video pipeline

Given this simplified real-time video processing code snippet, which step is most likely causing a bottleneck increasing latency?

def process_frame(frame):
    preprocessed = heavy_preprocessing(frame)
    prediction = model_inference(preprocessed)
    postprocessed = simple_postprocessing(prediction)
    return postprocessed

for frame in video_stream:
    output = process_frame(frame)
    display(output)

A The <code>model_inference</code> step because it runs the trained model on the frame.

B The <code>heavy_preprocessing</code> step because it likely involves complex image transformations.

C The <code>simple_postprocessing</code> step because it modifies the prediction output.

D The <code>display</code> function because rendering output is slow.

💡 Hint

Consider which step usually takes the most computation in a real-time ML pipeline.

Model inference typically requires the most computation and is the main bottleneck in real-time pipelines, especially if the model is large or not optimized.