HOG Features Research Articles

Face mask detection algorithm based on HSV+ HOG features and SVM

Face mask detection algorithm based on HSV+ HOG features and SVM

Dance Movement Recognition Based on Multimodal Environmental Monitoring Data.

Fine motion recognition is a challenging topic in computer vision, and it has been a trendy research direction in recent years. This study combines motion recognition technology with dance movements and the problems such as the high complexity of dance movements and fully considers the human body's self-occlusion. The excellent motion recognition content in the dance field was studied and analyzed. A compelling feature extraction method was proposed for the dance video dataset, segmented video, and accumulated edge feature operation. By extracting directional gradient histogram features, a set of directional gradient histogram feature vectors is used to characterize the shape features of the dance video movements. A dance movement recognition method is adopted based on the fusion direction gradient histogram feature, optical flow direction histogram feature, and audio signature feature. Three components are combined for dance movement recognition by a multicore learning method. Experimental results show that the cumulative edge feature algorithm proposed in this study outperforms traditional models in the recognition results of HOG features extracted from images. After adding edge features, the description of the dance movement shape is more effective. The algorithm can guarantee a specific recognition rate of complex dance movements. The results also verify the effectiveness of the movement recognition algorithm in this study for dance movement recognition.

Spatial–Spectral Weighted and Regularized Tensor Sparse Correlation Filter for Object Tracking in Hyperspectral Videos

Hyperspectral video camera captures spatial, spectral and temporal information of moving objects. Traditional object tracking methods developed for color videos have been applied to hyperspectral videos after compressing hundreds of spectral bands into three, which does not fully utilize the wealth spectral information. In order to address this issue, we present a tensor sparse correlation filter with a spatial-spectral weighted regularizer for object tracking. First, tensor processing is employed to reduce the spectral differences of homogeneous background, thereby producing robust spectral structure features. Second, a spatial-spectral weighted regularizer is designed in the correlation filter framework to penalize filter template by suppressing spectral features dissimilar to the center pixel in tracking. Third, a sparse constraint term and tracking context information are incorporated to suppress unexpected peaks in the response map. Finally, a reformulated stacked HOG feature extractor and a two-dimensional adaptive scale search strategy are developed to further improve the tracker’s feature discrimination and scale adaptation capability. Experimental results demonstrate that the proposed method achieves superior tracking performance than traditional correlation filter-based trackers.

InSAR Terrain Matching Algorithm Based on Morphologically Enhanced HOG Features

InSAR Terrain Matching Algorithm Based on Morphologically Enhanced HOG Features

Non- Destructive and Cost-Effective Silkworm Cocoon Gender Classification with Hog Feature Descriptors

Non- Destructive and Cost-Effective Silkworm Cocoon Gender Classification with Hog Feature Descriptors

Diagnosis and Classification Decision Analysis of Overheating Defects of Substation Equipment Based on Infrared Detection Technology

Substation equipment is not only the main part of the power grid but also the essential part to ensure the development of the national economy and People's Daily life of one of the important infrastructure. How to ensure its normal operation and find the sudden failure has become a hot issue to be solved urgently. For thermal fault diagnosis needs to classify and identify different power equipment first, this paper designed an SVM infrared image classifier, which can effectively identify three types of common power equipment. The classifier extracts HOG features from the infrared images of power equipment processed by the above segmentation and combines them with SVM multiclassification to achieve the purpose of improving the recognition accuracy. The experiment uses the classifier to identify three kinds of equipment, and the results show that the comprehensive recognition accuracy of the classifier is more than 95.3%, which is better than the traditional classification method and meets the demand for classification accuracy. In this paper, the traditional method of relative temperature difference is improved by using the temperature data of the infrared image, which can automatically judge the thermal failure level of electric power equipment. Experiments show that the diagnosis system designed in this paper can classify faults and give treatment suggestions while judging whether there are thermal faults for three types of power equipment, which verifies the feasibility and effectiveness of the substation infrared diagnosis technology designed in this paper.

Scale-invariant histogram of oriented gradients: novel approach for pedestrian detection in multiresolution image dataset

This paper proposes a scale-invariant histogram of oriented gradients (SI-HOG) for pedestrian detection. Most of the algorithms for pedestrian detection use the HOG as the basic feature and combine other features with the HOG to form the feature set, which is usually applied with a support vector machine (SVM). Hence, the HOG feature is the most efficient and fundamental feature for pedestrian detection. However, the HOG feature produces feature vectors of different lengths for different image resolutions; thus, the feature vectors are incomparable for the SVM. The proposed method forms a scale-space pyramid wherein the histogram bin is calculated. Thus, the gradient information from all the scales is encapsulated in a single fixed-length feature vector. The proposed method is also combined with color and texture features. The proposed approach is tested on three established benchmark pedestrian datasets: INRIA, NICTA, and Daimler. An improvement of ≥4.5% in the miss rate is achieved for all the three datasets considered. We also show that the SI-HOG can be applied to multiresolution datasets for which the HOG feature cannot be applied. Additionally, the MapReduce model is used to obtain the same outcome. The results indicate that the proposed approach outperforms the pedestrian-detection methods considered in this work.

Aircraft Cabin Decoration Style Recommendation Algorithm Based on Machine Vision

To improve the accuracy of the aircraft cabin decoration style recommendation algorithm, an aircraft cabin decoration style recommendation algorithm based on machine vision is proposed. The outline diagram of aircraft cabin decoration style is described with the help of hog features, and the clustering observation is carried out through K-means clustering algorithm to complete the feature description of aircraft cabin decoration style. The background pixels of aircraft cabin decoration are determined with the help of Gaussian mixture model method, the style feature points to be measured are updated gradient, the input aircraft cabin decoration style sequence image is compared with the background image, the difference of statistical information such as gray characteristics of pixels or straight image is analyzed, and the background preprocessing of aircraft cabin decoration style is completed. This study analyzes the basic principle and operation process of machine vision system, obtains the aircraft cabin decoration style image with the help of machine vision, calculates the weight value of recommended users, determines the number of decoration style recommendations, and completes the aircraft cabin decoration style recommendation. The experimental results show that the proposed method can effectively improve the accuracy of aircraft cabin decoration style recommendations, and it is feasible.

Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method

It's well-known that industrial safety is now a top concern. Nowadays, accidents caused by flammable gases occur frequently in our everyday lives. Gas cylinders, which are used for household purposes, wide range of businesses, and vehicles are often reported to be on the verge of exploding. Explosions have left a large number of individuals seriously wounded or could also be lethal in certain cases. This project's goal is to use a HOG features for SVM classifier which is used to identify pipeline gas leaks and keep tabs on them. In addition, the system utilises an image processing technique to identify pipeline fractures. Early detection and identification of pipeline flaws is a predominant aspect of this study. According to the suggested design, the robot capture the image down the pipe, looking for any signs of gas leakage by the Eddy Current method. This type of recognition has proved superior to other traditional methods. The methods with efficiency parameters and the results were compared and are tabulated in the results section. In the future, the data in the course of detection could be sent through GSM to a mobile application.

Multi-Feature Broad Learning System for Image Classification

A multi-feature broad learning system (MFBLS) is proposed to improve the image classification performance of broad learning system (BLS) and its variants. The model is characterized by two major characteristics: multi-feature extraction method and parallel structure. Multi-feature extraction method is utilized to improve the feature-learning ability of BLS. The method extracts four features of the input image, namely convolutional feature, K-means feature, HOG feature and color feature. Besides, a parallel architecture that is suitable for multi-feature extraction is proposed for MFBLS. There are four feature blocks and one fusion block in this structure. The extracted features are used directly as the feature nodes in the feature block. In addition, a “stacking with ridge regression” strategy is applied to the fusion block to get the final output of MFBLS. Experimental results show that MFBLS achieves the accuracies of 92.25%, 81.03%, and 54.66% on SVHN, CIFAR-10, and CIFAR-100, respectively, which outperforms BLS and its variants. Besides, it is even superior to the deep network, convolutional deep belief network, in both accuracy and training time on CIFAR-10. Code for the paper is available at https://github.com/threedteam/mfbls .

Urdu text in natural scene images: a new dataset and preliminary text detection.

Text detection in natural scene images for content analysis is an interesting task. The research community has seen some great developments for English/Mandarin text detection. However, Urdu text extraction in natural scene images is a task not well addressed. In this work, firstly, a new dataset is introduced for Urdu text in natural scene images. The dataset comprises of 500 standalone images acquired from real scenes. Secondly, the channel enhanced Maximally Stable Extremal Region (MSER) method is applied to extract Urdu text regions as candidates in an image. Two-stage filtering mechanism is applied to eliminate non-candidate regions. In the first stage, text and noise are classified based on their geometric properties. In the second stage, a support vector machine classifier is trained to discard non-text candidate regions. After this, text candidate regions are linked using centroid-based vertical and horizontal distances. Text lines are further analyzed by a different classifier based on HOG features to remove non-text regions. Extensive experimentation is performed on the locally developed dataset to evaluate the performance. The experimental results show good performance on test set images. The dataset will be made available for research use. To the best of our knowledge, the work is the first of its kind for the Urdu language and would provide a good dataset for free research use and serve as a baseline performance on the task of Urdu text extraction.

A moving ISAR-object recognition using pi-sigma neural networks based on histogram of oriented gradient of edge

ABSTRACT Detection and classification with traditional neural networks methods such as multilayer perceptron (MLP), feed forward network and back propagation neural networks show several drawbacks including the rate of convergence and the incapacity facing the problems of size of the image especially for radar images. As a result, these methods are being replaced by other evolutional classification methods such as Higher Order Neural Networks (HONN) (Functional Link Artificial Neural Network (FLANN), Pi Sigma Neural Network (PSNN), Neural Network Product Unit (PUNN) and Neural Network of the Higher Order Processing Unit. So, in this paper, we address radar object detection and classification problems with a new strategy by using PSNN and a new proposed method HOGE for edges features extraction based on morphological operators and histogram of oriented gradient. Thus, in order to recognise radar object, we extract HOG features of the object region and classify our target with PSNN. The HOGE features vector is used as input of pi-sigma NN. The proposed method was tested and confirmed based on experiments through the use of 2D and 3D ISAR images.

An abnormal event detection method based on the Riemannian manifold and LSTM network

In this paper, we propose an abnormal event detection method based on the combination of Riemannian manifold and LSTM network. We divide the video into a series of non overlapping regions, and extract HOG and HOF as original appearance and motion features. Because the existing abnormal event detection methods pay little attention to the changes rate of appearance and motion, we propose a new feature called Riemannian manifold distance feature that can represent the topological relationship of features at different moment in the same position and capture the change rate of the current feature. Once the change rate of the current features changes, the topological relationship of features will change. We use ISOMAP algorithm to embed manifold into low dimensional Euclidean space. In the training phase, the dimension-reduced features of the same position at different historical moment are used as the input of each time step of the LSTM network, and the dimension-reduced feature of the current moment is used as expected output. And in this stage, we only use the normal video, so the LSTM will learn the changing rules of the features contained in normal events. In the testing phase, we use LSTM network to predict current dimension-reduced HOG and HOF features. After that, we can obtain the Riemannian manifold distance feature of actual dimension-reduced feature and predicted dimension-reduced feature. In order to detect abnormal events, we propose an abnormal score, which is used to measure whether the appearance/ movement and the change rate of them are close to the expectation. The abnormal score can detect the abnormal event according to dimension-reduced appearance feature, dimension-reduced motion feature, appearance Riemannian manifold distance feature and motion Riemannian manifold distance feature. Experiments on four benchmark datasets show the competitive performance of our method with the state-of-the-art methods.

Traffic sign recognition based on HOG feature extraction

The substantial increase in the number of motor vehicles in recent years has caused many traffic safety problems and has aroused widespread concern. As the basis of intelligent vehicle environment perception and a necessary condition for realizing the functions of assisted driving system, traffic sign recognition is of great significance for realizing automatic driving of vehicles, improving intelligent transportation systems, and promoting the development of smart cities.This paper mainly identifies traffic signs, using histogram of gradient feature extraction method. The image is collected and preprocessed by a vision sensor. The color threshold segmentation method and morphological processing are used to reduce the interference of the background area and enhance the contour of the sign area. Finally, HOG method is used to collect the gradient of each pixel point in the cell unit or the direction histogram of the edge to identify traffic signs. Through MATALB simulation, it is obtained that the HOG image feature extraction method has high accuracy, small error and short recognition time, which shows the effectiveness of the algorithm.

Robust Real Time Multiple Human Detection and Tracking for Automatic Visual Surveillance System

Detection of human for visual surveillance system provides most important rule for advancement in the design of future automation systems. Human detection and tracking are important for future automatic visual surveillance system (AVSS). In this paper we have proposed a flexible technique for proper human detection and tracking for the design of AVSS. We used graph cut for segment human as a foreground image by eliminating background, extract some feature points by using HOG, SVM classifier for proper classification and finally we used particle filter for tracking those of detected human. Our system can easily detect and track humans in poor lightening conditions, color, size, shape, and clothing due to the use of HOG feature descriptor and particle filter. We use graph cut based segmentation technique, therefore our system can handle occlusion at about 88%. Due to the use of HOG to extract features our system can properly work in indoor as well as outdoor environments with 97.61% automatic human detection and 92% automatic human detection and tracking accuracy of multiple human

Knowledge extraction for automatic driving control by using local correlation features

In recent years, with the rapid development of artificial intelligence technology, more and more companies have shifted their attention to the field of automatic driving. The target detection is one of the core technologies in the field of automatic driving, which can perceive the environment by using distributed sensors and affect the vehicle driving control. How to describe vehicle image is particularly important in target detection for automatic driving. In this paper, we combine edge information, HOG features and local association features to describe the targets in image for automatic driving. Then, the extracted features are used to learn a classifier for further vehicle detection. The experimental results show that the proposed method can achieve better prevision, recall, and F1‐measure than merely using edge features or HOG features. The proposed method can extract more comprehensive features for vehicle detection.

Research on pedestrian detection technology based on the SVM classifier trained by HOG and LTP features

In pedestrian detection, it is often affected by factors such as changes in the position of moving targets, changes in illumination, and complex background interference. Therefore, the traditional pedestrian feature detection method is affected by the complex environment, which will cause a certain deviation in the detection accuracy. This study combines hog and LTP (Local Trinary Patterns) feature detection technology to improve the efficiency of pedestrian detection. Constructing a pedestrian detection system based on SVM (Support Vector Machine) classifier trained by hog and LTP features, and constructs a pedestrian detection system according to the actual needs. Moreover, this paper designs a pedestrian detection experiment based on HOG (Histogram of Oriented Gradient) and LTP feature training SVM classifier. In addition, this paper uses the weighted fusion method to fuse the color map features with the depth map features, and finally uses the classifier to detect pedestrians. Experiments show that the new pedestrian detection system has a certain effect.

Garbage image recognition and classification based on hog feature and SVM-Boosting

Due to the diversity of garbage types in our daily life, we will encounter many difficulties in the process of classification. In this regard, I combine hog features and boosting algorithm to develop a SVM classification method. Firstly, the input image is preprocessed to make the image more recognizable. Secondly, the hog algorithm is used to extract the features of the image. Finally, the classification device is trained, and the relevant information is sent to the image set. On this basis, the classification situation is detected. The final results show that the classification efficiency of the algorithm is as high as 95% or even more, which is about 10% higher than that of single SVM classification method. It can accurately classify garbage and has certain feasibility.

CLASSIFICATION OF MAMMOGRAMS HAVING ARCHITECTURAL DISTORTION AND SPICULATED MASSES USING TEXTURE FEATURES

Architectural distortion is the third most common mammographic appearance of nonpalpable breast cancer, representing nearly 6% of abnormalities detected on screening mammography. Although its prevalence on mammography is small compared with calcication or visible mass, architectural distortion is also more difcult to diagnose because it can be subtle and variable in presentation. Early detection of breast cancer is possible by nding architectural distortion in monographic images. Spiculated masses account for about 14% of biopsied lesions and about 81% of these are malignant. Current CAD systems are dramatically better at detecting microcalcications than masses. The sensitivity is considerably lower for Spiculated Masses that are rated as "subtle" by radiologists Moreover, since current systems were devised with masses and calcications in mind, they don’t perform as well on other, less prevalent but still clinically signicant lesion types. In this paper, we propose a computer aided diagnosis system for distinguishing abnormal mammograms with architectural distortion or spiculated masses from normal mammograms. Five types of texture features GLCM, GLRLM, fractal texture, spectral texture and HOG features for the regions of suspicion are extracted. Support vector machine has been used as classier in this work. The proposed system yielded an overall accuracy of 97.29% for mammogram images collected from mini-MIAS database which is better as compared to existing methods.

Deep Spatial/temporal-level feature engineering for Tennis-based action recognition

Tennis has becoming an increasingly popular sport throughout the world. Tennis motion recognition based on 3D video has attracted more and more attention in recent years. The algorithm based on dynamic time warping takes into account the timing sequence information of movements and can solve the uncertainty of human movement at temporal level. By increasing the training samples, the efficiency will decrease accordingly. This work presents a tennis action recognition framework based on action standard sequence. The 3D action video samples are incorporated into action sequences by feature extraction, wherein the action standard sequences are encoded as a sequence averaging optimization problem under the dynamic time normalization metric. The dynamic time normalization barycenter averaging algorithm (DBA) is leveraged to solve this problem. For the tennis scenery with significant differences in the action categories, we study the standard sequence learning of multiple actions, and accordingly propose a DBA-K-means clustering algorithm for unsupervised learning. Herein, a human tennis action recognition by integrating feature optimization and image similarity is proposed. The three dimensional reduction methods, including principal component analysis (PCA),PCA + Pearson, and PCA+ Spearman, were compared to prove that PCA+ Pearson correlation coefficient had the best dimensional reduction effect. Meanwhile, the global feature eight-star model is combined with the local feature HOG feature after dimensionally reduced to fully represent human movements. The similarity between pairwise adjacent frames of images was calculated. The statistical weight of single frame SVM classification results within a discriminant period is adaptively allocated, and finally the body pose recognition results are classified twice. Experiments on standard data set KTH show that the recognition accuracy of this algorithm is 94.5%, which is better than other methods. It has a good application value in the field of video human motion recognition. Also, we have demonstrated that this method can further improve the efficiency and accuracy of action recognition. Effective feature extraction is beneficial to improve the accuracy of subsequent human action recognition.