Using Histogram of Oriented Gradients (HOG) for Object Detection (2023)

FAQs

What detection is Histogram of Oriented Gradients suitable for? ›

Histogram of Oriented Gradients, also known as HOG, is a feature descriptor like the Canny Edge Detector, SIFT (Scale Invariant and Feature Transform) . It is used in computer vision and image processing for the purpose of object detection.

In this tutorial, we will use Histogram of Oriented Gradient (HOG) feature descriptor based linear SVM to create a person detector. We will first create a person classifier and then use this classifier with a sliding window to identify and localize people in an image.

The disadvantage is that the final descriptor vector grows larger, thus taking more time to extract and to train using a given classifier.

Histogram of Oriented Gradients(HOG), one of the well-known image processing algorithms, is a feature descriptor that is used for extracting essential features and shapes of a particular object within an image such as edges and textures.

The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image.

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.

Most Popular Object Detection Algorithms. Popular algorithms used to perform object detection include convolutional neural networks (R-CNN, Region-Based Convolutional Neural Networks), Fast R-CNN, and YOLO (You Only Look Once).

Especially, HOG shows good performance for human detection [11-13]. However, it has a disadvantage that is very sensitive to image rotation. Therefore, HOG is not good choice for classification of textures or objects which can often be detected as rotated image.

The YOLO framework (You Only Look Once) on the other hand, deals with object detection in a different way. It takes the entire image in a single instance and predicts the bounding box coordinates and class probabilities for these boxes.

Avoid using a histogram if:

You want to compare the specific values of individual data points.

What are three limitations of a histogram? ›

Disadvantages of Histogram

Here are the cons/drawback of histogram: Not allow you to read exact values because data is grouped into categories. It uses only with continuous data. In Histogram, it is not easy to compare two data sets.

Histograms can sometimes be misleading because of the way they conflate data into larger bins. For example, the graph showing year of composition shows five tragedies written between 459 and 450 BCE.

The HOG with a pre-processing framework achieves a higher accuracy rate of 92.5% and the non-processing method accuracy rate of 88.13%, improving the accuracy rate of 4.37%.

The Histogram of Oriented Gradients (HOG) is a method used in computer vision and image processing to describe the features of a given piece of data. It considers the number of times the gradient orientation in a localized part of an image occurs. Support vector machine (SVM) is also known as the discriminative method.

Local Binary Pattern (LBP) is an effective texture descriptor for images which thresholds the neighboring pixels based on the value of the current pixel [12]. LBP descriptors efficiently capture the local spatial patterns and the gray scale contrast in an image.

What is HOG and how it works ? HOG is a feature descriptor used to extract the features pixel by pixel with the help of gradients. This is primarily used for face detection, recognition and object detection. HOG works on grey scale images.

The HOG person detector uses a sliding detection window which is moved around the image. At each position of the detector window, a HOG descriptor is computed for the detection window. This descriptor is then shown to the trained SVM, which classifies it as either “person” or “not a person”.

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. HOG is robust for object detection because object shape is characterized using the local intensity gradient distribution and edge direction.

HOG, or Histogram of Oriented Gradients, is a feature descriptor that is often used to extract features from image data. It is widely used in computer vision tasks for object detection.

Is HOG A CNN? ›

Great success has been achieved recently on general object recognition by means of deep neural networks. Thus we are inspired to inspect the effectiveness of deep neural network on face recognition. This paper presents a deep neural network architecture referred as HOG-CNN for face recognition.

Object detection in videos can also be difficult because of the fast speed required of object detection algorithms to accurately classify and localise important objects in motion to meet real-time video processing. Another significant problem facing object detection is the limited amount of annotated data.

Local outlier factor (LOF)

Local outlier factor is probably the most common technique for anomaly detection. This algorithm is based on the concept of the local density. It compares the local density of an object with that of its neighbouring data points.

C++ is considered to be the fastest programming language, which is highly important for faster execution of heavy AI algorithms. A popular machine learning library TensorFlow is written in low-level C/C++ and is used for real-time image recognition systems. Advantages: A collection of AI libraries and tools.

The disadvantages of a histogram are that they are extremely focused on the number of "bins," or lines, and are heavily affected by the maximum and minimum of the variable. Altering the max and min can alter the look of a graph dramatically, which can be misleading.

It doesn't allow to detect relevant values.

In general, when a variable contains some frequent values, we need to be aware of it. However, histograms don't allow to do that, because they are based on intervals, and intervals “hide” individual values.

The histogram is a popular graphing tool. It is used to summarize discrete or continuous data that are measured on an interval scale. It is often used to illustrate the major features of the distribution of the data in a convenient form.

What are the advantages and disadvantages of using histograms? ›

The advantage of a frequency histogram over a frequency distribution is that it allows you to visually compare data. The frequency histogram has the disadvantage of being more complex, requiring more time and effort to produce than the frequency distribution.

The advantage of a frequency histogram is, that it is visually strong. Histograms are usually preferable to stem and leaf diagrams in large data sets. The disadvantage of a stem leaf diagram is not visual. The disadvantage of histogram frequency is cannot read exact values because data is grouped into categories.

It shows the center, the spread, the skewness of the data, the presence of outliers, and the presence of multiple modes in the data.

Common mistakes

Try several bin size, it can lead to very different conclusions. Don't use weird color sheme. It does not give any more insight. Don't confound it with a barplot.

The main advantages of a histogram are its simplicity and versatility. It can be used in many different situations to offer an insightful look at frequency distribution. For example, it can be used in sales and marketing to develop the most effective pricing plans and marketing campaigns.

An alternative to both histograms and boxplots is to use density plots.

On average about 57% of a hog make it from the pen to the pan. A 250 lb. hog will yield approximately 144 lbs of retail cuts. Around 28% of a hog's live weight is inedible product re- moved during the slaughter and dressing procedure bringing our 250 lb.

A heavily muscled hog will have a plump, firm, deep, thick meaty ham and long rump. When viewed from the rear, the lower part of the ham should be the widest part of the hog. The ham should extend well down onto the hock. The hog should be muscular over the shoulders with a wide chest and the loin wide along the back.

Convolutional Neural Networks (CNNs) is the most popular neural network model being used for image classification problem.

Which model should I use for image classification? ›

VGG16 is a pre-trained CNN model which is used for image classification. It is trained on a large and varied dataset and fine-tuned to fit image classification datasets with ease. Now, import a VGG16 model. You must initialize the model and add input and output layers.

Computer Vision: For image classification using deep learning, a rule of thumb is 1,000 images per class, where this number can go down significantly if one uses pre-trained models [6].

Given the above-mentioned parameters, LBPH works as follows; A data set is created by taking images with a camera or taking images that are saved, and then provisioning a unique identifier or name of the person in the image and then adding the images to a database.

For calculating the LBP, the LBP code for each pixel is calculated and the histogram of LBP codes is constructed as the LBP feature. To calculate the lbp code, for each pixel p, the 8 neighbours of the center pixel are compared with the pixel p and the neighbours x are assigned a value 1 if x ≥ p. Fig.

Local binary pattern network: A deep learning approach for face recognition.

It can help detect any unusual observations (outliers) or any gaps in the data. A histogram divides up the range of possible values in a data set into classes or groups.

Histograms are particularly helpful to analyze the frequency distribution of sample data. In a statistical experiment, frequency distribution is the number of observations that belong to a particular category (or “bin” in histogram terminology).

Histogram: A histogram is the best way to check univariate data — data containing a single variable — for outliers. A histogram divides the range of values into various groups (or buckets), and then shows the frequency — how many times the data falls into each group — through a bar graph.

In general, a histogram can be used whenever there's a need to display a comparison of the distribution of certain numerical data in various ranges of intervals. Histogram examples can help an audience see and understand quickly and easily essential meanings and patterns related to a large amount of data.

Histograms are not only useful in determining the minimum data point, maximum data point, and the median. But it is also used to find out the standard median of the data. The range of the chart from left to right, that is also called the class width of the chart, can be found out by using a histogram.

Why is hog better for 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. HOG is robust for object detection because object shape is characterized using the local intensity gradient distribution and edge direction.

Farrow-to-finish operations raise hogs from birth to slaughter weight, about 240-270 pounds. Feeder pig producers raise pigs from birth to about 10-60 pounds, then generally sell them for finishing. Feeder pig finishers buy feeder pigs and grow them to slaughter weight.

Byproducts (edible offal (including variety meats), inedible offal, hides and skins, blood, fats, and tallow) include all parts of a live animal that are not part of the dressed carcass and constitute about 30 percent of the liveweight of hogs and about 44 percent of the live- weight of cattle.

Click Data > Data Analysis > Histogram > OK. Under Input, select the input range (your data), then select the bin range. Under Output options, choose an output location. To show the data in descending order of frequency, click Pareto (sorted histogram).

Scatter plots and box plots are the most preferred visualization tools to detect outliers. Scatter plots — Scatter plots can be used to explicitly detect when a dataset or particular feature contains outliers.

A histogram with an overlaid box plot are shown below. The outlier is identified as the largest value in the data set, 1441, and appears as the circle to the right of the box plot. Outliers should be investigated carefully.