Part 11/17:
Effective computer vision models depend on diverse, representative datasets. Variations in lighting, camera angles, backgrounds, and object appearances mean data must encompass different scenarios, times of day, and environmental conditions. Failure to do so risks model bias and poor generalization.
Automating Data Acquisition
Manual data collection is infeasible at scale, especially across hundreds or thousands of cameras. Techniques recommended include:
Regular periodic captures to establish baseline datasets.
Triggered captures based on activity detection (e.g., motion detection).
Leveraging transaction or operational triggers, such as capturing images at checkout starts.
Feedback-driven data augmentation based on model performance issues.