After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection.
Histogram of oriented gradients (HOG) is a feature descriptor used to detect objects in computer vision and image processing. Human Detection,” IEEE Computer Society Conference on Computer

Normalisation introduces better invariance to illumination, However, since HOG captures local intensity gradients and edge directions, it also makes for a good texture descriptor.The HOG descriptor returns a real-valued feature vector. Since 10 degrees is half way between 0 and 20, the vote by the pixel splits evenly into the two bins.There is one more detail to be aware of. Histograms of Oriented Gradients Carlo Tomasi A useful question to ask of an image is whether it contains one or more instances of a certain object: a person, a face, a car, and so forth. However, we can also use HOG descriptors for quantifying and representing both shape and texture. The cornerstone of the HOG descriptor algorithm is that appearance of an object can be modeled by the distribution of intensity gradients inside rectangular regions of an image:Implementing this descriptor requires dividing the image into small connected regions called cells, and then for each cell, computing a histogram of oriented gradients for the pixels within each cell. Now we are ready to calculate the HOG descriptor for this image patch.The paper by Dalal and Triggs also mentions gamma correction as a preprocessing step, but the performance gains are minor and so we are skipping the step.To calculate a HOG descriptor, we need to first calculate the horizontal and vertical gradients; after all, we want to calculate the histogram of gradients. In practice we use gamma (power law) compression, either

And then I extracted a HOG feature vector (using the exact same HOG parameters) from Image B, which had different dimensions (i.e. If you continue to use this site we will assume that you are happy with it. You can see the histogram has a lot of weight near 0 and 180 degrees, which is just another way of saying that in the patch gradients are pointing either up or down.Before I explain how the histogram is normalized, let’s see how a vector of length 3 is normalized.Let’s say we have an RGB color vector [ 128, 64, 32 ]. And if our feature vectors are not the same dimensionality, then they cannot be compared for similarity. Not only is the representation more compact, calculating a histogram over a patch makes this represenation more robust to noise. We have selected a patch of size 100×200 for calculating our HOG feature descriptor. We can then accumulate these histograms across multiple cells to form our feature vector.Dalal and Triggs also demonstrated that we can perform Now, let’s review each of the steps for computing the HOG descriptor.This normalization step is entirely optional, but in some cases this step can improve performance of the HOG descriptor.
further resistance to illumination variations. Abstract: This brief studies the redundancy in the image processing pipeline for histogram of oriented gradients (HOG) feature extraction. These logos will serve as our Alright, enough talk. All views expressed on this site are my own and do not represent the opinions of OpenCV.org or any entity whatsoever with which I have been, am now, or will be affiliated. This code should look fairly similar to our code from the Let’s go ahead and extract HOG features from our training set:Using this image path, we are able to extract the make of the car on From there, we’ll perform a bit of pre-processing and prepare the car logo to be described using the Histogram of Oriented Gradients descriptor. This may seem redundant but it improves the performance. We start with the basic techniques like Viola Jones face detector to some of the advanced techniques like Single Shot Detector.Visit our discussion forum to ask any question and join our communityUsing Histogram of Oriented Gradients (HOG) for Object Detection Let’s start coding up this example.

This is easily achieved by filtering the image with the following kernels.Next, we can find the magnitude and direction of gradient using the following formulaIf you are using OpenCV, the calculation can be done using the function Notice, the x-gradient fires on vertical lines and the y-gradient fires on horizontal lines. Algorithms that answer this question are called object detectors. For the patch above, it looks like thisIn our representation, the y-axis is 0 degrees.