Real-time processing patterns in Computer Vision - Model Metrics & Evaluation

Metrics & Evaluation - Real-time processing patterns

Which metric matters for Real-time processing patterns and WHY

In real-time computer vision, the key metrics are latency and throughput. Latency measures how fast the model gives a result after receiving input, important for instant reactions like self-driving cars. Throughput measures how many frames or images the system can process per second, crucial for smooth video analysis. Accuracy is still important but must be balanced with speed to keep the system responsive.

Confusion matrix or equivalent visualization

For classification tasks in real-time, the confusion matrix helps understand errors:

      | Predicted Positive | Predicted Negative |
      |--------------------|--------------------|
      | True Positive (TP)  | False Positive (FP) |
      | False Negative (FN) | True Negative (TN)  |
    

Example with 100 frames processed:

      TP = 70, FP = 10, TN = 15, FN = 5
      Total = 100 frames
    

Precision = 70 / (70 + 10) = 0.875
Recall = 70 / (70 + 5) = 0.933

Precision vs Recall tradeoff with concrete examples

In real-time face recognition for security, high precision means fewer false alarms (wrongly flagging someone). High recall means catching almost every authorized person. If precision is low, many innocent people get flagged, causing frustration. If recall is low, some authorized people are missed, causing access problems. The system must balance these based on the use case.

For example, a door lock system may prioritize precision to avoid locking out users, while a surveillance system may prioritize recall to catch all suspicious faces.

What "good" vs "bad" metric values look like for this use case

Good metrics for real-time processing:

• Latency under 100 milliseconds for instant feedback
• Throughput of 30+ frames per second for smooth video
• Precision and recall above 85% for reliable detection

Bad metrics would be:

• Latency over 500 milliseconds causing noticeable delay
• Throughput below 10 frames per second causing choppy video
• Precision or recall below 60%, leading to many errors

Metrics pitfalls

• Ignoring latency: A model with high accuracy but slow response is not useful in real-time.
• Overfitting: High accuracy on training data but poor real-time performance.
• Data leakage: Testing on data too similar to training inflates metrics.
• Accuracy paradox: High accuracy on imbalanced data can be misleading.
• Not measuring throughput: Missing how many frames the system can handle.

Self-check question

Your real-time object detection model has 98% accuracy but 12% recall on detecting pedestrians. Is it good for production? Why or why not?

Answer: No, it is not good. Despite high accuracy, the very low recall means the model misses most pedestrians, which is dangerous in real-time scenarios like autonomous driving. Catching all pedestrians (high recall) is critical for safety.

Key Result

In real-time processing, balancing low latency and high recall is key for safe and responsive computer vision systems.