scholarly journals Video Data Extraction and Processing for Investigation of Vehicles’ Impact on the Asphalt Deformation Through the Prism of Computational Algorithms

2020 ◽  
Vol 37 (6) ◽  
pp. 899-906
Author(s):  
Sabahudin Vrtagić ◽  
Edis Softić ◽  
Mirza Ponjavić ◽  
Željko Stević ◽  
Marko Subotić ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Data Extraction ◽  
Video Data ◽  
Digital Data ◽  
Vehicle Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Small Data ◽  
Video File ◽  
Linear Svm

There are numerous algorithms and solutions for car or object detection as humanity is aiming towards the smart city solutions. Most solutions are based on counting, speed detection, traffic accidents and vehicle classification. The mentioned solutions are mostly based on high-quality videos, wide angles camera view, vehicles in motion, and are optimized for good visibility conditions intervals. A novelty of the proposed algorithm and solution is more accurate digital data extraction from video file sources generated by security cameras in Bosnia and Herzegovina from M18 roadway, but not limited only to that particular source. From the video file sources, data regarding number of vehicles, speed, traveling direction, and time intervals for the region of interest will be collected. Since finding contours approach is effective only on objects that are mobile, and because the application of this approach on traffic junctions did not yield desired results, a more specific approach of classification using a combination of Histogram of Oriented Gradients (HOG) and Support Vector Machines (Linear SVM) has shown to be more appropriate as the original source data can be used for training where the main benefit is the preservation of local second-order interactions, providing tolerance to local geometric misalignment and ability to work with small data samples. The features of the objects within a frame are extracted first by standardizing the feature variables and then computing the first order gradients of the frame. In the next stage, an encoding that remains robust to small changes while being sensitive to local frame content is produced. Finally, the HOG descriptors are generated and normalized again. In this way the channel histogram and spatial vector becomes the feature vector for the Linear SVM classifier. With the following parameters and setup system accuracy was around 85 to 95%. In the next phase, after cleaning protocols on collected data parameters, data will be used to research asphalt deformation effects.

scholarly journals Classification of Action Based Video using Heterogeneous Feature Extraction and SVM

2019 ◽  
Vol 8 (11) ◽  
pp. 1887-1892
Keyword(s):  
Optical Flow ◽  
Video Sequence ◽  
Human Action ◽  
Video Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Video Frames ◽  
Integral Role ◽  
Heterogeneous Feature

Action recognition (AR) plays a fundamental role in computer vision and video analysis. We are witnessing an astronomical increase of video data on the web and it is difficult to recognize the action in video due to different view point of camera. For AR in video sequence, it depends upon appearance in frame and optical flow in frames of video. In video spatial and temporal components of video frames features play integral role for better classification of action in videos. In the proposed system, RGB frames and optical flow frames are used for AR with the help of Convolutional Neural Network (CNN) pre-trained model Alex-Net extract features from fc7 layer. Support vector machine (SVM) classifier is used for the classification of AR in videos. For classification purpose, HMDB51 dataset have been used which includes 51 Classes of human action. The dataset is divided into 51 action categories. Using SVM classifier, extracted features are used for classification and achieved best result 95.6% accuracy as compared to other techniques of the state-of- art.v

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 Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

2003 ◽  
Vol 15 (7) ◽  
pp. 1667-1689 ◽  
Author(s):  
S. Sathiya Keerthi ◽  
Chih-Jen Lin
Keyword(s):  
Support Vector Machines ◽  
Model Selection ◽  
Heuristic Method ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Rbf Kernel ◽  
Linear Svm ◽  
Generalization Errors

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyper parameters: the penalty parameter C and the kernel width σ. This letter analyzes the behavior of the SVM classifier when these hyper parameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.

scholarly journals SSVEP-based brain-computer interface for computer control application using SVM classifier

2018 ◽  
Vol 7 (4) ◽  
pp. 2722
Author(s):  
Raymond Sutjiadi ◽  
Timothy John Pattiasina ◽  
Resmana Lim
Keyword(s):  
Brain Computer Interface ◽  
Computer Control ◽  
Occipital Lobe ◽  
Ease Of Use ◽  
Computer Interface ◽  
Support Vector ◽  
Svm Classifier ◽  
Linear Svm ◽  
Control Application ◽  
The Brain

In this research, a Brain Computer Interface (BCI) based on Steady State Visually Evoked Potential (SSVEP) for computer control applications using Support Vector Machine (SVM) is presented. For many years, people have speculated that electroencephalographic activities or other electrophysiological measures of brain function might provide a new non-muscular channel that can be used for sending messages or commands to the external world. BCI is a fast-growing emergent technology in which researchers aim to build a direct channel between the human brain and the computer. BCI systems provide a new communication channel for disabled people. Among many different types of the BCI systems, the SSVEP based has attracted more attention due to its ease of use and signal processing. SSVEPs are usually detected from the occipital lobe of the brain when the subject is looking at a twinkling light source. In this paper, SVM is used to classify SSVEP based on electroencephalogram data with proper features. Based on the experiment utilizing a 14-channel Electroencephalography (EEG) device, 80 percent of accuracy can be reached by our SSVEP-based BCI system using Linear SVM Kernel as classification engine. 

scholarly journals A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 139
Author(s):  
Tanmay Basu ◽  
Simon Goldsworthy ◽  
Georgios V. Gkoutos
Keyword(s):  
Language Processing ◽  
Systematic Reviews ◽  
Data Extraction ◽  
Research Question ◽  
Relevant Literature ◽  
Support Vector ◽  
Svm Classifier ◽  
Geometric Errors ◽  
Training Corpus ◽  
Different Types

The objective of systematic reviews is to address a research question by summarizing relevant studies following a detailed, comprehensive, and transparent plan and search protocol to reduce bias. Systematic reviews are very useful in the biomedical and healthcare domain; however, the data extraction phase of the systematic review process necessitates substantive expertise and is labour-intensive and time-consuming. The aim of this work is to partially automate the process of building systematic radiotherapy treatment literature reviews by summarizing the required data elements of geometric errors of radiotherapy from relevant literature using machine learning and natural language processing (NLP) approaches. A framework is developed in this study that initially builds a training corpus by extracting sentences containing different types of geometric errors of radiotherapy from relevant publications. The publications are retrieved from PubMed following a given set of rules defined by a domain expert. Subsequently, the method develops a training corpus by extracting relevant sentences using a sentence similarity measure. A support vector machine (SVM) classifier is then trained on this training corpus to extract the sentences from new publications which contain relevant geometric errors. To demonstrate the proposed approach, we have used 60 publications containing geometric errors in radiotherapy to automatically extract the sentences stating the mean and standard deviation of different types of errors between planned and executed radiotherapy. The experimental results show that the recall and precision of the proposed framework are, respectively, 97% and 72%. The results clearly show that the framework is able to extract almost all sentences containing required data of geometric errors.

scholarly journals Voice Feature Extraction for Gender and Emotion Recognition

2021 ◽  
Vol 40 ◽  
pp. 03008
Author(s):  
Madhu M. Nashipudimath ◽  
Pooja Pillai ◽  
Anupama Subramanian ◽  
Vani Nair ◽  
Sarah Khalife
Keyword(s):  
Feature Extraction ◽  
Data Extraction ◽  
Principal Component ◽  
Feature Reduction ◽  
Healthcare Sector ◽  
Support Vector ◽  
Svm Classifier ◽  
Human Machine Interaction ◽  
Interaction Domain ◽  
Voice Activity Detector

Voice recognition plays a key function in spoken communication that facilitates identifying the emotions of a person that reflects within the voice. Gender classification through speech is a popular Human Computer Interaction (HCI) method on account that determining gender through computer is hard. This led to the development of a model for "Voice feature extraction for Emotion and Gender Recognition". The speech signal consists of semantic information, speaker information (gender, age, emotional state), accompanied by noise. Females and males have specific vocal traits because of their acoustical and perceptual variations along with a variety of emotions which bring their own specific perceptions. In order to explore this area, feature extraction requires pre-processing of data, which is necessary for increasing the accuracy. The proposed model follows steps such as data extraction, pre-processing using Voice Activity Detector(VAD), feature extraction using Mel-Frequency Cepstral Coefficient(MFCC), feature reduction by Principal Component Analysis(PCA) and Support Vector Machine (SVM) classifier. The proposed combination of techniques produced better results which can be useful in healthcare sector, virtual assistants, security purposes and other fields related to Human Machine Interaction domain.

A Comprehensive Study on the Recognition of Gurmukhi Script

2020 ◽  
Vol 17 (6) ◽  
pp. 2674-2677
Author(s):  
Sandhya Sharma ◽  
Sheifali Gupta ◽  
Neeraj Kumar
Keyword(s):  
Nearest Neighbor ◽  
Text Recognition ◽  
Support Vector ◽  
Svm Classifier ◽  
Mlp Neural Network ◽  
The People ◽  
Linear Svm ◽  
Gurmukhi Script ◽  
Exchange Data ◽  
Comprehensive Study

Nowadays, we process all the important information of our lives electronically. Due to the involvement of computers in every sphere there may be a need to develop some efficient and fast techniques so that records can be easily transferred between people and computer systems. Offline text recognition provides an interface between humans and computers. Many researchers are working to recognize the text of Indian scripts like Bangla, Devanagari, Gurmukhi etc. but it is still a challenge to exchange data between people and computers due to the different writing style of the people and very little work has been done for Gurmukhi. In this article different accuracy results are reviewed which are achieved by different researchers using different classification techniques. Various classifiers for the recognition of characters like Support Vector Machine (SVM) based classifier (Upper zone classifier and Lower zone classifier), Hidden Markov Model (HMM) by using a set of features of the normalized x–y traces of the stroke, DCT2 feature set using Linear SVM classifier, Polynomial SVM with iDCT2 features, Multi layered perceptron (MLP) neural network and Knearest neighbor (KNN) etc. classifiers have been used.

scholarly journals Voice Feature Extraction for Gender and Emotion Recognition

2021 ◽  
Vol 9 (5) ◽  
pp. 17-22
Author(s):  
Vani Nair ◽  
Pooja Pillai ◽  
Anupama Subramanian ◽  
Sarah Khalife ◽  
Dr. Madhu Nashipudimath
Keyword(s):  
Feature Extraction ◽  
Data Extraction ◽  
Principal Component ◽  
Feature Reduction ◽  
Healthcare Sector ◽  
Support Vector ◽  
Svm Classifier ◽  
Human Machine Interaction ◽  
Interaction Domain ◽  
Voice Activity Detector

Voice recognition plays a key role in spoken communication that helps to identify the emotions of a person that reflects in the voice. Gender classification through speech is a widely used Human Computer Interaction (HCI) as it is not easy to identify gender by computer. This led to the development of a model for “Voice feature extraction for Emotion and Gender Recognition”. The speech signal consists of semantic information, speaker information (gender, age, emotional state), accompanied by noise. Females and males have different voice characteristics due to their acoustical and perceptual differences along with a variety of emotions which convey their own unique perceptions. In order to explore this area, feature extraction requires pre- processing of data, which is necessary for increasing the accuracy. The proposed model follows steps such as data extraction, pre- processing using Voice Activity Detector (VAD), feature extraction using Mel-Frequency Cepstral Coefficient (MFCC), feature reduction by Principal Component Analysis (PCA) and Support Vector Machine (SVM) classifier. The proposed combination of techniques produced better results which can be useful in the healthcare sector, virtual assistants, security purposes and other fields related to the Human Machine Interaction domain. 

scholarly journals Cursor Movement Detection in Brain-Computer-Interface Systems Using the Hybrid K-Means Clustering Method and LSVM

2020 ◽  
Author(s):  
Leila Mohammadi ◽  
zahra einalou ◽  
Hamidreza Hosseinzadeh ◽  
Mehrdad Dadgostar
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Support Vector ◽  
Svm Classifier ◽  
Clustering Method ◽  
Feature Extraction And Selection ◽  
New Feature ◽  
Linear Svm ◽  
The Right

Abstract In this study, we present the detection of the up- and downward as well as the right- and leftward motion of cursor based on feature extraction. Feature Extraction and selection for finding the proper classifier among the data mining methods are of great importance. In the proposed method, the hybrid K-means clustering algorithm and the linear support vector machine (LSVM) classifier have been used for extracting the important features and detecting the cursor motion. In this algorithm, the K-means clustering method is used to recognize the available hidden patterns in each of the four modes (up, down, left, and right). The identification of these patterns can raise the accuracy of classification. The membership degree of each feature vector in the proposed new patterns is considered as a new feature vector corresponding to the previous feature vector and then, the cursor motion is detected using the linear SVM classifier. The database of the Karadeniz Technical University of Turkey has been used in the present article. Applying the proposed method for data based on the hold-up cross validation causes the accuracy of the classifier in the up- and downward and left- and rightward movements in each person to increase by 2–10%.

scholarly journals Training Data Extraction and Object Detection in Surveillance Scenario

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2689 ◽  
Author(s):  
Artur Wilkowski ◽  
Maciej Stefańczyk ◽  
Włodzimierz Kasprzak
Keyword(s):  
Public Space ◽  
Data Extraction ◽  
Criminal Activity ◽  
Training Data ◽  
Support Vector ◽  
Small Data ◽  
Data Sets ◽  
Surveillance Systems ◽  
Automatic Data ◽  
Small Data Sets

Police and various security services use video analysis for securing public space, mass events, and when investigating criminal activity. Due to a huge amount of data supplied to surveillance systems, some automatic data processing is a necessity. In one typical scenario, an operator marks an object in an image frame and searches for all occurrences of the object in other frames or even image sequences. This problem is hard in general. Algorithms supporting this scenario must reconcile several seemingly contradicting factors: training and detection speed, detection reliability, and learning from small data sets. In the system proposed here, we use a two-stage detector. The first region proposal stage is based on a Cascade Classifier while the second classification stage is based either on a Support Vector Machines (SVMs) or Convolutional Neural Networks (CNNs). The proposed configuration ensures both speed and detection reliability. In addition to this, an object tracking and background-foreground separation algorithm is used, supported by the GrabCut algorithm and a sample synthesis procedure, in order to collect rich training data for the detector. Experiments show that the system is effective, useful, and applicable to practical surveillance tasks.

Sign in / Sign up

Export Citation Format

Share Document