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.

Efficient Feature Descriptor using Gabor Filter and Principal Component Analysis for Glaucoma Diagnosis

2021 ◽  
pp. 6787-6794
Author(s):  
Anisha Rebinth, Dr. S. Mohan Kumar
Keyword(s):  
Principal Component Analysis ◽  
Gabor Filter ◽  
Principal Component ◽  
Component Analysis ◽  
Disease Diagnosis ◽  
Image Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Glaucoma Diagnosis

An automated Computer Aided Diagnosis (CAD) system for glaucoma diagnosis using fundus images is developed. The various glaucoma image classification schemes using the supervised and unsupervised learning approaches are reviewed. The research paper involves three stages of glaucoma disease diagnosis. First, the pre-processing stage the texture features of the fundus image is recorded with a two-dimensional Gabor filter at various sizes and orientations. The image features are generated using higher order statistical characteristics, and then Principal Component Analysis (PCA) is used to select and reduce the dimension of the image features. For the performance study, the Gabor filter based features are extracted from the RIM-ONE and HRF database images, and then Support Vector Machine (SVM) classifier is used for classification. Final stage utilizes the SVM classifier with the Radial Basis Function (RBF) kernel learning technique for the efficient classification of glaucoma disease with accuracy 90%.

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

2021 ◽  
Vol 17 ◽  
pp. 93-98
Author(s):  
LAKHYADEEP KONWAR ◽  
ANJAN KUMAR TALUKDAR ◽  
KANDARPA KUMAR SARMA
Keyword(s):  
Particle Filter ◽  
Surveillance System ◽  
Graph Cut ◽  
Visual Surveillance ◽  
Human Detection ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Tracking Accuracy ◽  
Detection And Tracking ◽  
Automation Systems

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

scholarly journals Landmark Points Detection in Case of Human Facial Tracking and Detection

2019 ◽  
Vol 9 (2) ◽  
pp. 3769-3776
Keyword(s):  
Face Detection ◽  
Error Detection ◽  
Mental States ◽  
Support Vector ◽  
Svm Classifier ◽  
Biometric Recognition ◽  
Essential Point ◽  
Facial Expression Analysis ◽  
Detection And Tracking ◽  
Facial Landmark

This paper describes the human facial landmark points detection is very important in the field of image processing as face detect, face identifies, face re-construct, face corners alignment, different head pose and facial expression analysis. Facial landmark is an essential point for applying face processing operation ranging from biometric recognition to mental states. In this paper, Haar cascading face detection technique is used to face detection and tracking. Histogram of Oriented Gradients (hog) has been used for 68 landmark points detection in case of human tracking and detection and support vector machine (svm) classifier are used for 68 landmark points detection for right-left eyebrow, left-right eye, nose, lips, chin, and jaw. The existing methods work effectively but many issues occur in detection as of different head poses, facial expressions, facial occlusion, illumination, colour, shadowing and self-shadowing etc. The performance of experimental results shows the advantages of our purposed method is highly accurate in terms of facial 68 landmark points tracking and detection and less error detection rate with the Multi-PIE database.

Localization and classification of human facial emotions using local intensity order pattern and shape-based texture features

2021 ◽  
pp. 1-21
Author(s):  
Tehmina Kalsum ◽  
Zahid Mehmood ◽  
Farzana Kulsoom ◽  
Hassan Nazeer Chaudhry ◽  
AR Khan ◽  
...  
Keyword(s):  
Hybrid Approach ◽  
Texture Features ◽  
Recognition System ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Proposed Model ◽  
Image Blurring ◽  
Local Intensity ◽  
Order Pattern

Facial emotion recognition system (FERS) recognize the person’s emotions based on various image processing stages including feature extraction as one of the major processing steps. In this study, we presented a hybrid approach for recognizing facial expressions by performing the feature level fusion of a local and a global feature descriptor that is classified by a support vector machine (SVM) classifier. Histogram of oriented gradients (HoG) is selected for the extraction of global facial features and local intensity order pattern (LIOP) to extract the local features. As HoG is a shape-based descriptor, with the help of edge information, it can extract the deformations caused in facial muscles due to changing emotions. On the contrary, LIOP works based on the information of pixels intensity order and is invariant to change in image viewpoint, illumination conditions, JPEG compression, and image blurring as well. Thus both the descriptors proved useful to recognize the emotions effectively in the images captured in both constrained and realistic scenarios. The performance of the proposed model is evaluated based on the lab-constrained datasets including CK+, TFEID, JAFFE as well as on realistic datasets including SFEW, RaF, and FER-2013 dataset. The optimal recognition accuracy of 99.8%, 98.2%, 93.5%, 78.1%, 63.0%, 56.0% achieved respectively for CK+, JAFFE, TFEID, RaF, FER-2013 and SFEW datasets respectively.

scholarly journals Feature Descriptor for Crowd Density Estimation

2019 ◽  
Author(s):  
Muhammad Bilal ◽  
Adwan Alanazi
Keyword(s):  
Density Estimation ◽  
State Of The Art ◽  
Texture Feature ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Crowd Density Estimation ◽  
Crowd Density ◽  
Feature Based ◽  
Crowd Monitoring

Crowd density estimation is an important task for crowd monitoring. Many efforts have been done to automate the process of estimating crowd density from images and videos. Despite series of efforts, it remains a challenging task. In this paper, we proposes a new texture feature-based approach for the estimation of crowd density based on Completed Local Binary Pattern (CLBP). We first divide the image into blocks and then re-divide the blocks into cells. For each cell, we compute CLBP and then concatenate them to describe the texture of the corresponding block. We then train a multi-class Support Vector Machine (SVM) classifier, which classifies each block of image into one of four categories, i.e. Very Low, Low, Medium, and High. We evaluate our technique on the PETS 2009 dataset, and from the experiments, we show to achieve 95% accuracy for the proposed descriptor.  We also compare other state-of-the-art texture descriptors and from the experimental results, we show that our proposed method outperforms other state-of-the-art methods.

scholarly journals A novel feature fusion based Human Action Recognition in 2D Videos

2018 ◽  
Vol 7 (2.20) ◽  
pp. 207 ◽  
Author(s):  
K Rajendra Prasad ◽  
P Srinivasa Rao
Keyword(s):  
Action Recognition ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Scale Invariant ◽  
Human Actions

Human action recognition from 2D videos is a demanding area due to its broad applications. Many methods have been proposed by the researchers for recognizing human actions. The improved accuracy in identifying human actions is desirable. This paper presents an improved method of human action recognition using support vector machine (SVM) classifier. This paper proposes a novel feature descriptor constructed by fusing the various investigated features. The handcrafted features such as scale invariant feature transform (SIFT) features, speed up robust features (SURF), histogram of oriented gradient (HOG) features and local binary pattern (LBP) features are obtained on online 2D action videos. The proposed method is tested on different action datasets having both static and dynamically varying backgrounds. The proposed method achieves shows best recognition rates on both static and dynamically varying backgrounds. The datasets considered for the experimentation are KTH, Weizmann, UCF101, UCF sports actions, MSR action and HMDB51.The performance of the proposed feature fusion model with SVM classifier is compared with the individual features with SVM. The fusion method showed best results. The efficiency of the classifier is also tested by comparing with the other state of the art classifiers such as k-nearest neighbors (KNN), artificial neural network (ANN) and Adaboost classifier. The method achieved an average of 94.41% recognition rate.  

CLASSIFYING FACIAL EXPRESSIONS USING LEVEL SET METHOD BASED LIP CONTOUR DETECTION AND MULTI-CLASS SUPPORT VECTOR MACHINES

2011 ◽  
Vol 25 (06) ◽  
pp. 835-862 ◽  
Author(s):  
ABU SAYEED MD. SOHAIL ◽  
PRABIR BHATTACHARYA
Keyword(s):  
Facial Expressions ◽  
Level Set Method ◽  
Level Set ◽  
Vision System ◽  
Facial Feature ◽  
Visual Observation ◽  
Contour Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Points

This paper describes a fully automated computer vision system for detection and classification of the seven basic facial expressions using Multi-Class Support Vector Machine (SVM). Facial expressions are communicated by subtle changes in one or more discrete features such as tightening of the lips, raising the eyebrows, opening and closing of eyes or certain combination of them, which can be identified through monitoring the changes in muscle movements (Action Units), located around the regions of mouth, eyes and eyebrows. For classifying facial expressions, an analytic representation of face with 15 feature points has been used that provides visual observation of the discrete features responsible for the seven basic facial expressions. Feature points from the region of mouth are detected by segmenting the lip contour applying a variational formulation of the level set method. A multidetector approach of facial feature point detection is utilized for identifying the feature-points from the regions of eyes, eyebrows and nose. Feature vectors composed of 15 features are then obtained with respect to the average representation of neutral face and are used to train a Multiclass SVM classifier. The proposed method has been tested over two different facial expression image databases and the average successful recognition rates of 92.04% and 86.33% have been achieved.

Research of Human Body Detection and Tracking Algorithm

2013 ◽  
Vol 791-793 ◽  
pp. 1023-1027
Author(s):  
Gang Zhang ◽  
Bin Ouyang ◽  
Lu Ming Yu ◽  
Lei Zhang
Keyword(s):  
Human Body ◽  
Moving Objects ◽  
Tracking Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Histogram Of Oriented Gradient ◽  
Detection And Tracking ◽  
Multi Media ◽  
Teaching Video ◽  
Tracking Technology

In this paper, the proposed algorithm regards the human body object character symbol using Support Vector Machine (SVM) classifier to train and classify Histogram of Oriented Gradient (HOG) features, which improve the accuracy of human body detection. We use optical flow tracking algorithm based on corner points of the contour for tracking. Kalman filter is regarded as the predictor to predict the size and location of the searching object. Also, the size and location of track window is real-time updated. In this paper, we present an object tracking algorithm for multi-media teaching video shoot. Target tracking technology is used for the video image processing analysis. By extracting moving object, we can get information in the subsequent frames to determine the trajectory and size of moving objects. After analysis of a large number of experiments, we can draw the conclusion that the algorithm is effective.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

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