Discover the most efficient solution to generate hourly visitor reports from security videos, grouping by hair style and color, using Amazon AWS Machine Learning Specialty techniques.
Table of Contents
Question
A beauty supply store wants to understand some characteristics of visitors to the store. The store has security video recordings from the past several years. The store wants to generate a report of hourly visitors from the recordings. The report should group visitors by hair style and hair color.
Which solution will meet these requirements with the LEAST amount of effort?
A. Use an object detection algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
B. Use an object detection algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair color.
C. Use a semantic segmentation algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
D. Use a semantic segmentation algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair.
Answer
C. Use a semantic segmentation algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
Explanation
The correct answer is C. Use a semantic segmentation algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
Semantic segmentation is better suited for precisely identifying and isolating the hair region in video frames compared to object detection. Object detection would only provide bounding boxes around the hair, while semantic segmentation classifies each pixel, enabling more accurate extraction of the hair region.
ResNet-50, a deep convolutional neural network, is more appropriate for classifying hair style and color than XGBoost. ResNet-50 can learn hierarchical features from the segmented hair images, capturing intricate patterns and details necessary for accurate classification. XGBoost, being a gradient boosting algorithm, is less suitable for this computer vision task.
Therefore, using semantic segmentation to isolate the hair region and ResNet-50 to classify hair style and color requires the least effort while providing the desired results for generating the hourly visitor report.
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