From the series: Student Competition: Computer Vision Training
Debanjana Mukherjee, MathWorks
Learn to detect objects using binary classifiers; template matching, histogram of gradients (HOG), and cascade object detection.
You’ll learn how Template Matching works and how to use it. The concepts behind HOG will be taught to prepare you for the Cascade Object Detector. The Cascade Object Detector is a robust detector which provides the option to use Haar, Local Binary Patterns (LBP), and HOG to detect objects within an image.
Template Matching and Cascade Object Detection are used to detect objects in an image that are aspect ratio and orientation invariant. Template Matching has an additional limitation that the object must be scale invariant. Both methods are useful for determining if an object is located within an image, and if so, where the object is located within the image.
Using these methods, student competition teams should be able to perform a variety of target identification tasks.
Note: The images used in this video are from the following website:
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