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.

Adaptive Algorithm Based on Wavelet and SVM for Turbopump Fault Detection

2014 ◽  
Vol 644-650 ◽  
pp. 994-1002
Author(s):  
Tao Hong ◽  
Shuo Yang
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Rocket Engine ◽  
Step Length ◽  
Training Sample ◽  
Detection Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Acceleration Signal ◽  
Liquid Rocket

An adaptive fault detection algorithm based on wavelet and SVM (Support Vector Machine) is proposed for LRE(Liquid Rocket Engine) turbopump real-time fault detection. The algorithm firstly divides the historical signals into some segments by reasonable step length. Then for each segment it gets M-layer detail signals through Daubechies wavelet transform. Thirdly it divides every layer into K average segments and calculates there RMS values, gets M RMS sequences of detail signals. After that it constructs M-dimensional RMS vector as fault feature by extracting RMS values at the same position in every RMS sequence, and extracts all the fault feature vectors of historical signal to construct SVM training sample set and then obtains SVM classifier. At last the classifier will be real-time updated by a reasonable method in the testing process to improve the classification accuracy. To validate the algorithm, a track of the vibration acceleration signal of a certain type of turbopump was chosen as the test object. The test results showed that the algorithm met its demands of accuracy and real-time performance.

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.

scholarly journals AN OPTIONAL THRESHOLD WITH SVM CLOUD DETECTION ALGORITHM AND DSP IMPLEMENTATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 771-777 ◽  
Author(s):  
Guoqing Zhou ◽  
Xiang Zhou ◽  
Tao Yue ◽  
Yilong Liu
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Threshold Method ◽  
Dsp Implementation ◽  
Svm Algorithm

This paper presents a method which combines the traditional threshold method and SVM method, to detect the cloud of Landsat-8 images. The proposed method is implemented using DSP for real-time cloud detection. The DSP platform connects with emulator and personal computer. The threshold method is firstly utilized to obtain a coarse cloud detection result, and then the SVM classifier is used to obtain high accuracy of cloud detection. More than 200 cloudy images from Lansat-8 were experimented to test the proposed method. Comparing the proposed method with SVM method, it is demonstrated that the cloud detection accuracy of each image using the proposed algorithm is higher than those of SVM algorithm. The results of the experiment demonstrate that the implementation of the proposed method on DSP can effectively realize the real-time cloud detection accurately.

Gaits Classification of Normal vs. Patients by Wireless Gait Sensor and Support Vector Machine (SVM) Classifier

2017 ◽  
Vol 5 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Taro Nakano ◽  
B.T. Nukala ◽  
J. Tsay ◽  
Steven Zupancic ◽  
Amanda Rodriguez ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Human Subjects ◽  
Wearable Sensors ◽  
Normal Subjects ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Dynamic ◽  
Linear Svm ◽  
Dynamic Gait

Due to the serious concerns of fall risks for patients with balance disorders, it is desirable to be able to objectively identify these patients in real-time dynamic gait testing using inexpensive wearable sensors. In this work, the authors took a total of 49 gait tests from 7 human subjects (3 normal subjects and 4 patients), where each person performed 7 Dynamic Gait Index (DGI) tests by wearing a wireless gait sensor on the T4 thoracic vertebra. The raw gait data is wirelessly transmitted to a near-by PC for real-time gait data collection. To objectively identify the patients from the gait data, the authors used 4 different types of Support Vector Machine (SVM) classifiers based on the 6 features extracted from the raw gait data: Linear SVM, Quadratic SVM, Cubic SVM, and Gaussian SVM. The Linear SVM, Quadratic SVM and Cubic SVM all achieved impressive 98% classification accuracy, with 95.2% sensitivity and 100% specificity in this work. However, the Gaussian SVM classifier only achieved 87.8% accuracy, 71.7% sensitivity, and 100% specificity. The results obtained with this small number of human subjects indicates that in the near future, the authors should be able to objectively identify balance-disorder patients from normal subjects during real-time dynamic gaits testing using intelligent SVM classifiers.

scholarly journals A Novel Changing Athlete Body Real-Time Visual Tracking Algorithm Based on Distractor-Aware SiamRPN and HOG-SVM

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 378 ◽  
Author(s):  
Mingwei Sheng ◽  
Weizhe Wang ◽  
Hongde Qin ◽  
Lei Wan ◽  
Jun Li ◽  
...  
Keyword(s):  
Real Time ◽  
Large Data ◽  
Video Frame ◽  
Support Vector ◽  
Key Frame ◽  
Histograms Of Oriented Gradients ◽  
Time Operation ◽  
Haar Feature ◽  
Real Time Operation ◽  
Detection Window

Athlete detection in sports videos is a challenging task due to the dynamic and cluttered background. Distractor-aware SiamRPN (DaSiamRPN) has a simple network structure and can be utilized to perform long-term tracking of large data sets. However, similarly to the Siamese network, the tracking results heavily rely on the given position in the initial frame. Hence, there is a lack of solutions for some complex tracking scenarios, such as running and changing of bodies of athletes, especially in the stage from squatting to standing to running. The Haar feature-based cascade classifier is involved to catch the key frame, representing the video frame of the most dramatic changes of the athletes. DaSiamRPN is implemented as the tracking method. In each frame after the key frame, a detection window is given based on the bounding box generated by the DaSiamRPN tracker. In the new detection window, a fusion method (HOG-SVM) combining features of Histograms of Oriented Gradients (HOG) and a linear Support-Vector Machine (SVM) is proposed for detecting the athlete, and the tracking results are updated in real-time by fusing the tracking results of DaSiamRPN and HOG-SVM. Our proposed method has reached a stable and accurate tracking effect in testing on men’s 100 m video sequences and has realized real-time operation.

scholarly journals Fast Pedestrian Recognition Based on Multisensor Fusion

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Hongyu Hu ◽  
Zhaowei Qu ◽  
Zhihui Li ◽  
Jinhui Hu ◽  
Fulu Wei
Keyword(s):  
Region Of Interest ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Multisensor Fusion ◽  
Feature Extraction Method ◽  
Urban Road ◽  
Histograms Of Oriented Gradients ◽  
Large Numbers ◽  
Candidate Regions

A fast pedestrian recognition algorithm based on multisensor fusion is presented in this paper. Firstly, potential pedestrian locations are estimated by laser radar scanning in the world coordinates, and then their corresponding candidate regions in the image are located by camera calibration and the perspective mapping model. For avoiding time consuming in the training and recognition process caused by large numbers of feature vector dimensions, region of interest-based integral histograms of oriented gradients (ROI-IHOG) feature extraction method is proposed later. A support vector machine (SVM) classifier is trained by a novel pedestrian sample dataset which adapt to the urban road environment for online recognition. Finally, we test the validity of the proposed approach with several video sequences from realistic urban road scenarios. Reliable and timewise performances are shown based on our multisensor fusing method.

scholarly journals A real-time drowsiness and fatigue recognition using support vector machine

2020 ◽  
Vol 9 (4) ◽  
pp. 584
Author(s):  
Nur Nabilah Abu Mangshor ◽  
Iylia Ashiqin Abdul Majid ◽  
Shafaf Ibrahim ◽  
Nurbaity Sabri
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Recognition Task ◽  
Support Vector ◽  
Svm Classifier ◽  
Face Expression ◽  
Exact Position ◽  
Sobel Edge Detection ◽  
Processing Techniques ◽  
Current Accuracy

<p>A drowsiness and fatigue problems among the drivers are the main factor that contributes to road accidents. These problems are vital to be resolved as they could contribute to damage of road facilities, vehicles and most importantly the loss of lives. In avoiding these matters, a proper mechanism is needed to alert the driver to stay awake throughout the driving journey. Thus, this study proposed a real-time prototype for recognizing the drowsiness and fatigue face expression of the driver. The methodology of this study involves facial features detection using Viola-Jones algorithm to detect the exact position of both left and right eyes and mouth. Next, based on the detected eyes and mouth beforehand, the segmentation processes performed on both eyes and mouth using Sobel edge detection to obtain facial regions. The feature extraction phase is conducted using shape-based feature to obtain the extraction values. Support vector machine (SVM) classifier is deployed for the recognition task. A total of 100 images are used during the testing stages. This study achieved a competetive result of 90.00% of accuracy. Yet, hybridization or integration of more image processing techniques will be performed in the future to improve the current accuracy obtained.</p>

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.

A Wearable Real-time Non-Contact Electrocardiogram System for Arrhythmia Detection and Classification (Preprint)

2020 ◽  
Author(s):  
Hongqiang Li ◽  
Sai Zhang ◽  
Shasha Zuo ◽  
Zhen Zhang ◽  
Binhua Wang ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Real Time ◽  
Predictive Value ◽  
Heart Diseases ◽  
Back Propagation ◽  
Feature Space ◽  
Primary Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Heart Health

BACKGROUND Driven by the increasing demand for potential patients to monitor their own heart health, wearable technology is increasingly helping people to better monitor their heart health status at a medical level. OBJECTIVE The aim of this study was to develop a flexible and non-contact wearable electrocardiogram system, which can achieve real-time monitoring and primary diagnosis. METHODS A flexible electrocardiogram (ECG) acquisition device (wearable ECG) is designed based on flexible front-end circuit and textile capacitive electrodes, which are based on a conductive textile instead of rigid metal plates. The multi-domain feature space consists of time-domain features and frequency-domain statistical features, which can be used for classification via a back-propagation neural network (BPNN) and a support vector machine (SVM), both of which are optimized using a genetic algorithm. RESULTS The BPNN classifier exhibits good performance, with an accuracy of 98.33%, a sensitivity of 98.33%, a specificity of 99.63% and a positive predictive value of 97.85%. The SVM classifier achieves a higher classification accuracy of 98.89% and also performs better than the BPNN classifier in terms of the sensitivity, specificity and positive predictive value, achieving values of 98.89%, 99.81% and 98.89%, respectively. CONCLUSIONS The experimental results reveal that there is a better classification effect of SVM when classifying normal heart rhythms and 8 types of arrhythmia. The proposed wearable ECG monitoring can aid in the primary diagnosis of certain heart diseases.

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