Histogram Of Oriented Gradients Algorithm Research Articles

A novel low-resource consumption and high-speed hardware implementation of HOG feature extraction on FPGA for human detection

In today’s increasingly complex traffic environment, pedestrian detection has become increasingly important. The Histogram of Oriented Gradients (HOG) algorithm has been proven to be highly efficient in pedestrian detection. This paper proposes a low-resource consumption, high-speed hardware implementation for HOG algorithm. In the case of a slight sacrifice in accuracy, it increases computational speed and reduces resource consumption. Experimental results demonstrate that the implementation achieves a speed of 0.933 pixels per clock cycle and consumes 4117 look-up tables and 4.5 Kbits of block RAMs while its accuracy decreases by 1.2% on the INRIA dataset and by 0.11% on the MIT dataset.

Analysis of the Possibility of Using Selected Tools and Algorithms in the Classification and Recognition of Type of Microstructure.

The aim of this research was to develop a solution based on existing methods and tools that would allow the automatic classification of selected images of cast iron microstructures. As part of the work, solutions based on artificial intelligence were tested and modified. Their task is to assign a specific class in the analyzed microstructure images. In the analyzed set, the examined samples appear in various zoom levels, photo sizes and colors. As is known, the components of the microstructure are different. In the examined photo, there does not have to be only one type of precipitate in each photo that indicates the correct microstructure of the same type of alloy, different shapes may appear in different amounts. This article also addresses the issue of data preparation. In order to isolate one type of structure element, the possibilities of using methods such as HOG (histogram of oriented gradients) and thresholding (the image was transformed into black objects on a white background) were checked. In order to avoid the slow preparation of training data, our solution was proposed to facilitate the labeling of data for training. The HOG algorithm combined with SVM and random forest were used for the classification process. In order to compare the effectiveness of the operation, the Faster R-CNN and Mask R-CNN algorithms were also used. The results obtained from the classifiers were compared to the microstructure assessment performed by experts.

Vision-Based HAR in UAV Videos Using Histograms and Deep Learning Techniques.

Activity recognition in unmanned aerial vehicle (UAV) surveillance is addressed in various computer vision applications such as image retrieval, pose estimation, object detection, object detection in videos, object detection in still images, object detection in video frames, face recognition, and video action recognition. In the UAV-based surveillance technology, video segments captured from aerial vehicles make it challenging to recognize and distinguish human behavior. In this research, to recognize a single and multi-human activity using aerial data, a hybrid model of histogram of oriented gradient (HOG), mask-regional convolutional neural network (Mask-RCNN), and bidirectional long short-term memory (Bi-LSTM) is employed. The HOG algorithm extracts patterns, Mask-RCNN extracts feature maps from the raw aerial image data, and the Bi-LSTM network exploits the temporal relationship between the frames for the underlying action in the scene. This Bi-LSTM network reduces the error rate to the greatest extent due to its bidirectional process. This novel architecture generates enhanced segmentation by utilizing the histogram gradient-based instance segmentation and improves the accuracy of classifying human activities using the Bi-LSTM approach. Experimental outcomes demonstrate that the proposed model outperforms the other state-of-the-art models and has achieved 99.25% accuracy on the YouTube-Aerial dataset.

Monitoring of butt weld penetration based on infrared sensing and improved histograms of oriented gradients

In the process of butt welding, achieving real-time monitoring of butt weld penetration is of great significance for improving forming quality. Currently, weld penetration detection methods based on weld pool vision are easily disturbed by arc lights. In this study, an innovative approach for butt weld penetration detection using infrared sensing and improved histograms of oriented gradients (HOG) is proposed. First, an improved HOG algorithm was studied to extract the temperature field distribution features of the welding heat-affected zone (HAZ) around the weld pool. Second, the feasibility of using the improved HOG feature to detect weld penetration was analysed, and a weld penetration detection model was established. Finally, the generalisation performance of the proposed weld penetration detection algorithm was confirmed, and the influence of the location and size of the detection area on the accuracy of the detection algorithm was analysed. The testing results indicate that the proposed detection algorithm exhibits high precision and excellent generalisation performance, and enhances the means of monitoring of butt weld penetration.

Automated Smart Attendance System Using Face Recognition

Abstract: In the human body, the face contains many important details, so the face is the most significant factor in identifying a person. There are several main ways to detect the presence of a person. Biometrics for capturing attendance. This is a time consuming process. In this paper, we will use a collection of rules to develop a model that classifies each character's face from the captured images. The HOG algorithm for recording student attendance. HOG (Histogram of Oriented Gradients) is one of the methods used to represent and classify images, is a common and effective method, and was chosen for its robustness against changes in pose and lighting. .. The proposed ASAS (Automated Smart Attendance System) captures images and compares them to the images stored in the databases. The database is updated during student enrollment using an automated process that also includes names and role numbers. Key Terms: HOG, python, ASAS

Face recognition Attendance system using HOG and CNN algorithm

Recognition of faces is one of the most useful applications and has a critical role in the technological field. Recognizing the face is a lively concern for authentication, specifically in the context of taking attendance. Attendance system using face recognition is a process of recognizing the profile of the person by using facial features supported by various computing technology and monitoring. The evolution of this process is focused on achieving the digitizing of the orthodox system of taking manual attendance. Current approaches for taking attendance are monotonous and inefficient. Manual records of attendance can be easily manipulated. The orthodox process of checking attendance such as current fingerprint or card scanning systems are susceptible to proxies. To tackle these issues, this paper has been proposed. The proposed system makes the utilization of various algorithms such as histogram of oriented Gradient (HOG), convolutional neural network (CNN) and support vector machine (SVM). After the face is recognized, the reports of attendance are going to be created, maintained and stored in excel format. The system is examined in various situations like illumination, head movements, and the variation of distance between the face and cameras. The proposed system was found to be efficient and reliable for marking attendance during a classroom with negligible time consumption and no manual work. This system is inexpensive as less installation is required.

A Fusion Approach Based on HOG and Adaboost Algorithm for Face Detection under Low- Resolution Images

Detecting human faces in low-resolution images is more difficult than high quality images because people appear smaller and facial features are not as clear as high resolution face images. Furthermore, the regions of interest are often impoverished or blurred due to the large distance between the camera and the objects which can decrease detection rate and increase false alarms. As a result, the performance of face detection (detection rate and the number of false positives) in low-resolution images can affect directly subsequent applications such as face recognition or face tracking. In this paper, a novel method, based on cascade Adaboost and Histogram of Oriented Gradients (HOG), is proposed to improve face detection performance in low resolution images, while most of researches have been done and tested on high quality images. The focus of this work is to improve the performance of face detection by increasing the detection rate and at the same time decreasing the number of false alarms. The concept behind the proposed combination is based on the a-priori rejection of false positives for a more accurate detection. In other words in order to increase human face detection performance, the first stage (cascade Adaboost) removes the majority of the false alarms while keeping the detection rate high, however many false alarms still exist in the final output. To remove existing false alarms, a stage (HOG+SVM) is added to the first stage to act as a verification module for more accurate detection. The method has been extensively tested on the Carnegie Melon University (CMU) database and the low-resolution images database. The results show better performance compared with existing techniques.

AUTOMATED ATTENDANCE SYSTEM WITH FACE RECOGNITION

Face is the representation of one’s identity. So, we have prepared an automated student attendance system based on face recognition. This system is very useful in daily life applications especially in security and surveillance systems. The security systems on airport uses face recognition to identify suspects and the CBI (CentralBureau of Investigation) and FBI (Federal Bureau of Investigation) uses face recognition for criminal investigations. In our project also video framing is performed by accessing the camera through user friendly interface. The Face is detected and segmented from the video frame by using HOG (Histogram of Oriented Gradient) algorithm. In the first step or we can say in pre-processing stage, scaling of the size of the image is performed in order to prevent or reduce the loss of information. Then in next step, the ‘median filtering’ is applied to remove noise followed by the conversion of colour imageinto grayscale image. After that, CLAHE (Contrast Limited Adaptive Histogram Equation) is applied on the images to enhance the contrastof the image. Overall, we have created a program in python that take theimage from the database and make all the necessary conversions for recognition and then verifies the image inthe videos or in the real time by accessing the camera through user friendly interface. After the successful matchis found then it marks the name and time of the person in attendance sheet.

Improved HOG Feature Vehicle Recognition Algorithm Based On Sliding Window

With the continuous development in recent years, machine learning has been extensively used in many application scenarios, especially in vehicle recognition. At present, the histogram of oriented gradients (HOG) algorithm has been widely used in vehicle recognition and facial recognition. This paper studies the HOG algorithm, introduces the vehicle recognition using the HOG algorithm, and introduces a detection method based on sliding window. In this research, an improved HOG algorithm based on sliding window is proposed for vehicle recognition and compared with the traditional HOG algorithm. In randomness test, the traditional recognition method often causes its HOG features to be trapped in a large number of invalid features due to the uncertainty of the vehicle distribution. This makes it difficult to be identified by the classifier, resulting in erroneous images, with an accuracy of only 83.33%. The optimized sliding window algorithm eliminates the influence of overlapping windows, and the number of accurately-recognized images is largely increased, with an accuracy of 91.02%.

The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study.

BackgroundAutism spectrum disorder (ASD) is a complex neurodevelopmental disorder with an unknown etiology. Early diagnosis and intervention are key to improving outcomes for patients with ASD. Structural magnetic resonance imaging (sMRI) has been widely used in clinics to facilitate the diagnosis of brain diseases such as brain tumors. However, sMRI is less frequently used to investigate neurological and psychiatric disorders, such as ASD, owing to the subtle, if any, anatomical changes of the brain.ObjectiveThis study aimed to investigate the possibility of identifying structural patterns in the brain of patients with ASD as potential biomarkers in the diagnosis and evaluation of ASD in clinics.MethodsWe developed a novel 2-level histogram-based morphometry (HBM) classification framework in which an algorithm based on a 3D version of the histogram of oriented gradients (HOG) was used to extract features from sMRI data. We applied this framework to distinguish patients with ASD from healthy controls using 4 datasets from the second edition of the Autism Brain Imaging Data Exchange, including the ETH Zürich (ETH), NYU Langone Medical Center: Sample 1, Oregon Health and Science University, and Stanford University (SU) sites. We used a stratified 10-fold cross-validation method to evaluate the model performance, and we applied the Naive Bayes approach to identify the predictive ASD-related brain regions based on classification contributions of each HOG feature.ResultsOn the basis of the 3D HOG feature extraction method, our proposed HBM framework achieved an area under the curve (AUC) of >0.75 in each dataset, with the highest AUC of 0.849 in the ETH site. We compared the 3D HOG algorithm with the original 2D HOG algorithm, which showed an accuracy improvement of >4% in each dataset, with the highest improvement of 14% (6/42) in the SU site. A comparison of the 3D HOG algorithm with the scale-invariant feature transform algorithm showed an AUC improvement of >18% in each dataset. Furthermore, we identified ASD-related brain regions based on the sMRI images. Some of these regions (eg, frontal gyrus, temporal gyrus, cingulate gyrus, postcentral gyrus, precuneus, caudate, and hippocampus) are known to be implicated in ASD in prior neuroimaging literature. We also identified less well-known regions that may play unrecognized roles in ASD and be worth further investigation.ConclusionsOur research suggested that it is possible to identify neuroimaging biomarkers that can distinguish patients with ASD from healthy controls based on the more cost-effective sMRI images of the brain. We also demonstrated the potential of applying data-driven artificial intelligence technology in the clinical setting of neurological and psychiatric disorders, which usually harbor subtle anatomical changes in the brain that are often invisible to the human eye.

Heuristics of Machine Learning for Home Intrusion Detection Application

Security has become a very vital role in present modern world. We need security for various applications and for our data. In particular security in home application is very crucial. So to serve this purpose in an efficient and easy manner we developed an intrusion system where in HOG (Histogram of Oriented Gradients) algorithm is used to detect person and an API to give alerts for intrusion. HOG is the Machine Learning (ML) algorithm used particularly for the person detection. The API used here is TWILIO which is the most suitable API for sending messages within seconds and accurately. Since every system is becoming automated we focused more on implementing the HOG and making the system to learn by itself and perform accurate results. In this paper we explained how HOG algorithm is implemented to detect the person entering the house and send the alerts as the person is detected. The accuracy of the model along with further developments that can be possible is given in detail.

DETECTING AND COUNTING COCONUT TREES IN PLEIADES SATELLITE IMAGERY USING HISTOGRAM OF ORIENTED GRADIENTS AND SUPPORT VECTOR MACHINE

This paper describes the detection of coconut trees using very-high-resolution optical satellite imagery. The satellite imagery used in this study was a panchromatic band of Pleiades imagery with a spatial resolution of 0.5 metres. The authors proposed the use of a histogram of oriented gradients (HOG) algorithm as the feature extractor and a support vector machine (SVM) as the classifier for this detection. The main objective of this study is to find out the parameter combination for the HOG algorithm that could provide the best performance for coconut-tree detection. The study shows that the best parameter combination for the HOG algorithm is a configuration of 3 x 3 blocks, 9 orientation bins, and L2-norm block normalization. These parameters provide overall accuracy, precision and recall of approximately 80%, 73% and 87%, respectively.

Human Detection On Foggy Images

This paper was discussing about the human detection using SVM combining weighted least square-filter (WLS), histograms of oriented gradients (HOG). The combination of HOG and SVM is a powerful approach for human detection, as it uses local strength gradients; it is hard to handle noisy and foggy images. For removing of noise or fog from this type of images, we used weighted least square (WLS) filter, and then HOG and SVM algorithms are used for human detection. Due to deprived weather conditions such as fog and haze, the acquired images will exhibit damaged visibility. This can be occurred because of the presence of the suspended particles and scatter of light between objects and the camera. So the image improvement and renewal methods are used to improve the quality of an image which provide strong image in poor weather condition and can extract features from the images not only when they had illumination variations but also when they are degraded with fog. At last, detected objects can be categorized into predefined groups of humans and other objects by using SVM classifier.

Classification and Recognition of Ovarian Cells Based on Two-Dimensional Light Scattering Technology.

Ovarian cancer is a very insidious malignant tumor. In order to detect ovarian cancer cells early, the classification and recognition of ovarian cancer cells is mainly studied by two-dimensional light scattering technology. Firstly, a single-cell two-dimensional light scattering pattern acquisition platform based on single-mode optical fiber illumination is designed to collect a certain number of two-dimensional light scattering patterns of ovarian cancer cells and normal ovarian cells. Then, the HOG (Histogram of Oriented Gradient) algorithm is used to extract shaving anisotropy feature of two-dimensional light scattering pattern. The results show that the accuracy of classification and identification of ovarian cancer cells by two-dimensional light scattering technology is 90.81%, which suggests that the specificity of cancer cells and normal cells can be characterized by two-dimensional light scattering technology.

Comparison of histograms of oriented gradients (HOG) and n-Row average subtraction (nRAS) using GprMax

Detection of underground objects remains an important task today, particularly when attempting to recover landmines. A Ground Penetrating Radar (GPR) sends short pulses in time domain to detect underground objects by recording the reflected signal. GPR traces acquired at different positions in space are combined together to form B-scan images. The presence of objects produce hyperbolic fluctuations in B-scan images that depend on the shape, dielectric properties, and depth of the sensed object, and also on the properties of the medium. Dielectric properties of some objects, like plastic objects, create very small fluctuations in GPR traces. To analyze the recorded patterns, efficient algorithms need to be developed and improved based on the inherent variations of the overall conditions. For detecting underground objects under noise, this paper compares the results of two algorithms: Histograms of Oriented Gradients (HOG), and n-Row Average Subtraction (nRAS). For tests, five different scenarios from two test configurations have been used. According to the results for all images in the first test configuration, HOG and 3RAS algorithms both increase Object Detection Ratio (ODR) from 88% to 93% while decreasing the False Alarm Rate (FAR) considerably. The accuracy is also tested for different image sizes. And for certain algorithms, lower resolution images result in better accuracy.

Pure FPGA Implementation of an HOG Based Real-Time Pedestrian Detection System.

In this study, we propose a real-time pedestrian detection system using a FPGA with a digital image sensor. Comparing with some prior works, the proposed implementation realizes both the histogram of oriented gradients (HOG) and the trained support vector machine (SVM) classification on a FPGA. Moreover, the implementation does not use any external memory or processors to assist the implementation. Although the implementation implements both the HOG algorithm and the SVM classification in hardware without using any external memory modules and processors, the proposed implementation’s resource utilization of the FPGA is lower than most of the prior art. The main reasons resulting in the lower resource usage are: (1) simplification in the Getting Bin sub-module; (2) distributed writing and two shift registers in the Cell Histogram Generation sub-module; (3) reuse of each sum of the cell histogram in the Block Histogram Normalization sub-module; and (4) regarding a window of the SVM classification as 105 blocks of the SVM classification. Moreover, compared to Dalal and Triggs’s pure software HOG implementation, the proposed implementation‘s average detection rate is just about 4.05% less, but can achieve a much higher frame rate.

A New Method of Histogram Computation for Efficient Implementation of the HOG Algorithm

In this paper we introduce a new histogram computation method to be used within the histogram of oriented gradients (HOG) algorithm. The new method replaces the arctangent with the slope computation and the classical magnitude allocation based on interpolation with a simpler algorithm. The new method allows a more efficient implementation of HOG in general, and particularly in field-programmable gate arrays (FPGAs), by considerably reducing the area (thus increasing the level of parallelism), while maintaining very close classification accuracy compared to the original algorithm. Thus, the new method is attractive for many applications, including car detection and classification.

A robust algorithm for detecting people in overhead views

In this research a human detection system is proposed in which people are viewed from an overhead camera with a wide angle lens. Due to perspective change a person can have different orientations and sizes at different positions in the scene relative to the optical centre. We exploit this property of the overhead camera and develop a novel algorithm which uses the variable size bounding boxes with different orientations, with respect to the radial distance of the center of the image. In these overhead view images we neither used any assumption about the pose or the visibility of a person nor imposed any restriction about the environment. When compare the results of proposed algorithm with a standard histogram of oriented gradient (HOG) algorithm, we achieve not only a huge gain in overall detection rate but also a significant improvement in reducing spurious detections per image. On average, 9 false detections occur per image. A new algorithm is proposed where transforming the image patch containing a person to remove positional dependency and then applying the HOG algorithm eliminates 98% of the spurious detections in noisy images from an industrial assembly line and detects people with a 95% efficiency.

HW/SW Co-Design of the HOG algorithm on a Xilinx Zynq SoC

An accurate and fast human detection is a crucial task for a wide variety of applications such as automotive and person identification. The histogram of oriented gradients (HOG) algorithm is one of the most reliable and applied algorithms for this task. However the HOG algorithm is also a compute intensive task. This paper presents three different implementations using the Zynq SoC that consists of an ARM processor and an FPGA. The first uses OpenCV functions and runs on the ARM processor. A speedup of 249×is achieved due to several optimizations that are implemented in this OpenCV-based HOG approach. The second is a HW/SW Co-Design implemented on the ARM processor and the FPGA. The third is completely implemented on the FPGA and optimized for an FPGA implementation to achieve the highest performance for high resolution images (1920×1080). This implementation achieves 39.6 fps which is a speedup of 503.9× compared to the OpenCV-based approach and 2× compared to this implementation with optimizations. The HW/SW Co-Design achieves a speedup of approximately 9× compared to an original HOG implementation running on the ARM processor.

An approach for facial expression classification

In this paper, a new method for facial expression classification is proposed, which uses the histograms of oriented gradients (HOG) algorithm to extract facial expression features and the sparse representation classifier (SRC) to classify facial expressions with a large variation of poses. The HOG algorithm was selected due to its effectiveness in picking up both local and global facial expression features in different orientations and scales, and the SRC was chosen because of its proven effectiveness in face recognition. A novelty of the proposed approach is that given a facial image for classification, its pose is determined first to select a pose-dependent dictionary for the SRC procedure. The paper also discusses ways of selecting parameters for improving the effectiveness of the HOG algorithm. The proposed method was applied to two multi-pose facial expression databases and satisfactory results were obtained for the majority of facial expressions under various poses.