Training and Validating Object Detectors

After generating ground truth data in part 1, Sebastian Castro and Connell D’Souza of MathWorks go over the workflow for using this labeled data to train and evaluate an aggregate channel features object detector. This is done using built-in MATLAB^® training functions.

Sebastian and Connell show you how to use built-in functions to create training and evaluation datasets from labeled ground truth data. Once created, the training dataset is used to train an object detector using a single line function.

The trained detector is then used on an independent video stream to identify the objects of interest. The results are compared against the ground truth for this independent video stream to evaluate the trained detector. Sebastian and Connell also discuss metrics for object detector evaluation. Download all the files used in this video from MATLAB Central's File Exchange

Additional Resources:

• Object Detection Using Faster R-CNN Deep Learning
• imageDatastore
• evaluateDetectionPrecision
• evaluateDetectionMissRate