Research on athlete training behavior based on improved support vector algorithm and target image detection

2020 ◽  
Vol 39 (4) ◽  
pp. 5725-5736
Author(s):  
Jiang Min
Keyword(s):  
Target Detection ◽  
Training Model ◽  
Detection Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Target Image ◽  
Classification Result ◽  
Detection And Tracking ◽  
Neural Network Algorithm ◽  
Fully Connected

In view of the defects and shortcomings of the traditional target detection and tracking algorithm in accurately detecting targets and targets in different scenarios, based on the current research status and technical level of target detection and tracking at home and abroad, this paper proposes a target detection algorithm and tracking method using neural network algorithm, and applies it to the athlete training model. Based on the Alex-Net network structure, this paper designs a three-layer convolutional layer and two layers of fully connected layers. The last layer is used as the input of the SVM classifier, and the target classification result is obtained by the SVM classifier. In addition, this article adds SPP-Layer between the convolutional layer and the fully connected layer, enabling the same dimension of the Feature Map to be obtained before the fully connected layer for different sized input images. The research results show that the proposed method has certain recognition effect and can be applied to athlete training.

scholarly journals A Mature-Tomato Detection Algorithm Using Machine Learning and Color Analysis

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2023 ◽  
Author(s):  
Guoxu Liu ◽  
Shuyi Mao ◽  
Jae Ho Kim
Keyword(s):  
Detection Algorithm ◽  
Substantial Improvement ◽  
Support Vector ◽  
Svm Classifier ◽  
Color Analysis ◽  
Scanning Method ◽  
False Color ◽  
Histograms Of Oriented Gradients ◽  
Coarse To Fine ◽  
Tomato Detection

An algorithm was proposed for automatic tomato detection in regular color images to reduce the influence of illumination and occlusion. In this method, the Histograms of Oriented Gradients (HOG) descriptor was used to train a Support Vector Machine (SVM) classifier. A coarse-to-fine scanning method was developed to detect tomatoes, followed by a proposed False Color Removal (FCR) method to remove the false-positive detections. Non-Maximum Suppression (NMS) was used to merge the overlapped results. Compared with other methods, the proposed algorithm showed substantial improvement in tomato detection. The results of tomato detection in the test images showed that the recall, precision, and F1 score of the proposed method were 90.00%, 94.41 and 92.15%, respectively.

Face liveness detection scheme with static and dynamic features

2018 ◽  
Vol 16 (02) ◽  
pp. 1840001 ◽  
Author(s):  
Lifang Wu ◽  
Yaowen Xu ◽  
Meng Jian ◽  
Xiao Xu ◽  
Wei Qi
Keyword(s):  
Support Vector ◽  
Svm Classifier ◽  
Dynamic Features ◽  
Detection Scheme ◽  
Liveness Detection ◽  
Current State ◽  
Significant Research ◽  
The Face ◽  
Frame Motion ◽  
Fully Connected

Face liveness detection is a significant research topic in face-based online authentication. The current face liveness detection approaches utilize either static or dynamic features, but not both. In fact, the dynamic and static features have different advantages in face liveness detection. In this paper, we propose a scheme combining dynamic and static features to capture merits of them for face liveness detection. First, the dynamic maps are captured from the inter-frame motion in the video, which investigates motion information of the face in the video. Then, with a Convolutional Neural Network (CNN), the dynamic and static features are extracted from the dynamic maps and the frame images, respectively. Next, in CNN, the fully connected layers containing the dynamic and static features are concatenated to form a fused feature. Finally, the fused features are used to train a binary Support Vector Machine (SVM) classifier, which classifies the frames into two categories, i.e. frame with real or fake face. Experimental results and the corresponding analysis demonstrate that the proposed scheme is capable of discovering face liveness by fusing dynamic and static features and it outperforms the current state-of-the-art face liveness detection approaches.

scholarly journals Research on a Fast Human-Detection Algorithm for Unmanned Surveillance Area in Bulk Ports

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Chao Mi ◽  
Xin He ◽  
Haiwei Liu ◽  
Youfang Huang ◽  
Weijian Mi
Keyword(s):  
Real Time ◽  
Computing Time ◽  
Training Sample ◽  
Detection Algorithm ◽  
Human Detection ◽  
Security Requirements ◽  
Support Vector ◽  
Svm Classifier ◽  
Histograms Of Oriented Gradients ◽  
Heavy Equipment

With the development of port automation, most operational fields utilizing heavy equipment have gradually become unmanned. It is therefore imperative to monitor these fields in an effective and real-time manner. In this paper, a fast human-detection algorithm is proposed based on image processing. To speed up the detection process, the optimized histograms of oriented gradients (HOG) algorithm that can avoid the large number of double calculations of the original HOG and ignore insignificant features is used to describe the contour of the human body in real time. Based on the HOG features, using a training sample set consisting of scene images of a bulk port, a support vector machine (SVM) classifier combined with the AdaBoost classifier is trained to detect human. Finally, the results of the human detection experiments on Tianjin Port show that the accuracy of the proposed optimized algorithm has roughly the same accuracy as a traditional algorithm, while the proposed algorithm only takes 1/7 the amount of time. The accuracy and computing time of the proposed fast human-detection algorithm were verified to meet the security requirements of unmanned port areas.

scholarly journals An IoT Based Smart Organic Drip Fertigation System for Eco-Friendly Agriculture using SVM Classifier and ThingSpeak

2020 ◽  
Vol 9 (4) ◽  
pp. 709-713
Keyword(s):  
Support Vector Machine ◽  
Soil Erosion ◽  
Support Vector ◽  
Svm Classifier ◽  
Chemical Fertilizers ◽  
Classification Result ◽  
Feature Vectors ◽  
Flow Feature ◽  
Classification Prediction ◽  
Drip Fertigation

a result, the proposed system helps in reducing soil erosion as only the required nutrients are injected via the drip system in order to reduce the usage of chemical fertilizers. In this paper, we use Support Vector Machine (SVM) to classify three (Temperature, Ph, Flow) feature vectors. The classification results will predict whether the obtained data is normal or abnormal and explore the accuracy of classification prediction by using SVM. Finally, the classification result obtained by applying SVM is uploaded to the ThingSpeak cloud.

scholarly journals Classifying smoke in laparoscopic videos using SVM

2017 ◽  
Vol 3 (2) ◽  
pp. 191-194 ◽  
Author(s):  
Tamer Abdulbaki Alshirbaji ◽  
Nour Aldeen Jalal ◽  
Lars Mündermann ◽  
Knut Möller
Keyword(s):  
Detection Algorithm ◽  
Classification Performance ◽  
Visual Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Smoke Detection ◽  
Lighting Conditions ◽  
Event Based ◽  
Laparoscopic Videos ◽  
Smoke Removal

AbstractSmoke in laparoscopic videos usually appears due to the use of electrocautery when cutting or coagulating tissues. Therefore, detecting smoke can be used for event-based annotation in laparoscopic surgeries by retrieving the events associated with the electrocauterization. Furthermore, smoke detection can also be used for automatic smoke removal. However, detecting smoke in laparoscopic video is a challenge because of the changeability of smoke patterns, the moving camera and the different lighting conditions. In this paper, we present a video-based smoke detection algorithm to detect smoke of different densities such as fog, low and high density in laparoscopic videos. The proposed method depends on extracting various visual features from the laparoscopic images and providing them to support vector machine (SVM) classifier. Features are based on motion, colour and texture patterns of the smoke. We validated our algorithm using experimental evaluation on four laparoscopic cholecystectomy videos. These four videos were manually annotated by defining every frame as smoke or non-smoke frame. The algorithm was applied to the videos by using different feature combinations for classification. Experimental results show that the combination of all proposed features gives the best classification performance. The overall accuracy (i.e. correctly classified frames) is around 84%, with the sensitivity (i.e. correctly detected smoke frames) and the specificity (i.e. correctly detected non-smoke frames) are 89% and 80%, respectively.

A New Waters Hole Detection and Tracking Method for UGV in Cross-Country Environment

2016 ◽  
Vol 30 (08) ◽  
pp. 1655024 ◽  
Author(s):  
Yibing Zhao ◽  
Feng Ding ◽  
Xuecai Yu ◽  
Ronghui Zhang ◽  
Xiumei Xiang
Keyword(s):  
Scale Transformation ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Feature Points ◽  
Remarkable Feature ◽  
Tracking Method ◽  
Detection And Tracking ◽  
Cross Country ◽  
Hole Detection

Environment perception is one of the important issues for unmanned ground vehicle (UGV). It is necessary to develop waters hole detection and tracking method in cross-country environment. This paper is related to the waters hole detection and tracking by using visual information. Image processing strategies based on support vector machine (SVM) and speeded up robust feature (SURF) methods are employed to detect and track waters hole. It focuses on how to extract the waters feature descriptor by exploring the machine learning algorithm. Based on the S/V color features and Gray Level Co-occurrence Matrix, the waters feature descriptor is extracted. The radial basis function (RBF) kernel function and the sampling-window size are determined by using the SVM classifier. The optimal parameters are obtained under the cross-validation conditions by the grid method. In terms of waters tracking, SURF feature matching method is applied to extract the remarkable feature points, then to observe the relation between feature point movement of adjacent frames and scale change ratio. Experiments show that SURF algorithm can still be effective to detect and match the remarkable feature points, against the negative effects of waters scale transformation and affine transform. The conclusion is that the computing speed of SURF algorithm is about three times faster than that of scale-invariant feature transform (SIFT) algorithm, and the comprehensive performance of SURF algorithm is better.

scholarly journals A Peak Detection Algorithm for Localization and Classification of Heart Sounds in PCG Signals using K-means Clustering

2021 ◽  
Author(s):  
Muhammad Zubair
Keyword(s):  
Detection Algorithm ◽  
Heart Sounds ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Classifier ◽  
Mel Frequency Cepstral Coefficients ◽  
Sound Classification ◽  
Clustering Model ◽  
Peak Detection Algorithm

Traditionally, the heart sound classification process is performed by first finding the elementary heart sounds of the phonocardiogram (PCG) signal. After detecting sounds S1 and S2, the features like envelograms, Mel frequency cepstral coefficients (MFCC), kurtosis, etc., of these sounds are extracted. These features are used for the classification of normal and abnormal heart sounds, which leads to an increase in computational complexity. In this paper, we have proposed a fully automated algorithm to localize heart sounds using K-means clustering. The K-means clustering model can differentiate between the primitive heart sounds like S1, S2, S3, S4 and the rest of the insignificant sounds like murmurs without requiring the excessive pre-processing of data. The peaks detected from the noisy data are validated by implementing five classification models with 30 fold cross-validation. These models have been implemented on a publicly available PhysioNet/Cinc challenge 2016 database. Lastly, to classify between normal and abnormal heart sounds, the localized labelled peaks from all the datasets were fed as an input to the various classifiers such as support vector machine (SVM), K-nearest neighbours (KNN), logistic regression, stochastic gradient descent (SGD) and multi-layer perceptron (MLP). To validate the superiority of the proposed work, we have compared our reported metrics with the latest state-of-the-art works. Simulation results show that the highest classification accuracy of 94.75% is achieved by the SVM classifier among all other classifiers.

A Detection Algorithm for Projectile Target

2014 ◽  
Vol 1044-1045 ◽  
pp. 972-975
Author(s):  
Zai Fei Shang ◽  
Chun Ping Wang
Keyword(s):  
Target Detection ◽  
Mean Shift ◽  
Detection Algorithm ◽  
Support Vector ◽  
Performance Simulation ◽  
Histogram Of Oriented Gradient ◽  
Mean Shift Algorithm ◽  
Small Target Detection ◽  
Time Performance ◽  
Vector Machines

For consistency of performance in the shape of the projectile targets, a projectile target detection algorithm is presented based on HOG (Histogram of Oriented Gradient) characterization algorithm. First, detecting the bullet image corner, and secondly, by Mean-shift algorithm improves the corner position accuracy and reduces the number of corner points, finally, applying support vector machines to extract the projectile targets. Compared with the traditional small target detection algorithm, the algorithm describes the targets more accurately, along with better real-time performance. Simulation, the projectile target detection rate of over 80% and verify the effectiveness of the algorithm.

scholarly journals Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images

2021 ◽  
Vol 18 (15) ◽  
pp. 8052
Author(s):  
Prabal Datta Barua ◽  
Nadia Fareeda Muhammad Gowdh ◽  
Kartini Rahmat ◽  
Norlisah Ramli ◽  
Wei Lin Ng ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Generation ◽  
X Ray ◽  
Deep Feature ◽  
Deep Networks ◽  
Fully Connected

COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application.

scholarly journals Visual Sequence Algorithm for Moving Object Tracking and Detection in Images

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Renzheng Xue ◽  
Ming Liu ◽  
Xiaokun Yu
Keyword(s):  
Target Detection ◽  
Difference Method ◽  
Moving Objects ◽  
Target Location ◽  
Mean Shift ◽  
Detection Algorithm ◽  
Moving Target ◽  
Size Change ◽  
Detection And Tracking ◽  
Frame Difference

Objective. The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed. Methods. An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target. Results. The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted. Conclusion. The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.

Sign in / Sign up

Export Citation Format

Share Document