Table of Contents
What is occlusion in object detection?
Occlusion often occurs when two or more objects come too close and seemingly merge or combine with each other. Image processing system with object tracking often wrongly track the occluded objects [6]. Sometimes, after occlusion, the system will wrongly identify the initially tracked object as a new object [7].
What detection is histogram of oriented gradients suitable for?
object detection
The Histogram of Oriented Gradients method (or HOG for short) is used for object detection and image recognition. HOG is based off of feature descriptors, which extract the useful information and discard the unnecessary parts.
How does Harris corner detection work?
Compared to the previous one, Harris’ corner detector takes the differential of the corner score into account with reference to direction directly, instead of using shifting patches for every 45-degree angles, and has been proved to be more accurate in distinguishing between edges and corners.
What is HOG DLIB?
This is based on the HOG (Histogram of Oriented Gradients) feature descriptor with a linear SVM machine learning algorithm to perform face detection. HOG is a simple and powerful feature descriptor. It is not only used for face detection but also it is widely used for object detection like cars, pets, and fruits.
What technique we use in HOG to be independent of lighting variations?
Step 4 : 16×16 Block Normalization Ideally, we want our descriptor to be independent of lighting variations. In other words, we would like to “normalize” the histogram so they are not affected by lighting variations.
How do HOG features work?
The HOG features are widely use for object detection. HOG decomposes an image into small squared cells, computes an histogram of oriented gradients in each cell, normalizes the result using a block-wise pattern, and return a descriptor for each cell.