Color-mapped contour gait image for cross-view gait recognition using deep convolutional neural network

2019 ◽  
Vol 18 (01) ◽  
pp. 1941012
Author(s):  
G. Merlin Linda ◽  
G. Themozhi ◽  
Sudheer Reddy Bandi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Receptive Fields ◽  
Deep Convolutional Neural Network ◽  
Color Mapping ◽  
Difference Of Gaussians ◽  
Gait Feature ◽  
Hysteresis Thresholding

In recent decades, gait recognition has garnered a lot of attention from the researchers in the IT era. Gait recognition signifies verifying or identifying the individuals by their walking style. Gait supports in surveillance system by identifying people when they are at a distance from the camera and can be used in numerous computer vision and surveillance applications. This paper proposes a stupendous Color-mapped Contour Gait Image (CCGI) for varying factors of Cross-View Gait Recognition (CVGR). The first contour in each gait image sequence is extracted using a Combination of Receptive Fields (CORF) contour tracing algorithm which extracts the contour image using Difference of Gaussians (DoG) and hysteresis thresholding. Moreover, hysteresis thresholding detects the weak edges from the total pixel information and provides more well-balanced smooth features compared to an absolute one. Second CCGI encodes the spatial and temporal information via color mapping to attain the regularized contour images with fewer outliers. Based on the front view of a human walking pattern, the appearance of cross-view variations would reduce drastically with respect to a change of view angles. This proposed work evaluates the performance analysis of CVGR using Deep Convolutional Neural Network (CNN) framework. CCGI is considered a gait feature for comparing and evaluating the robustness of our proposed model. Experiments conducted on CASIA-B database show the comparisons of previous methods with the proposed method and achieved 94.65% accuracy with a better recognition rate.

A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition

2020 ◽  
Author(s):  
Habiba Arshad ◽  
Muhammad Attique Khan ◽  
Muhammad Irfan Sharif ◽  
Mussarat Yasmin ◽  
João Manuel R. S. Tavares ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gait Recognition ◽  
Deep Convolutional Neural Network ◽  
Human Gait ◽  
Features Selection

scholarly journals Deep Large Margin Nearest Neighbor for Gait Recognition

2021 ◽  
Vol 30 (1) ◽  
pp. 604-619
Author(s):  
Wanjiang Xu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Video Surveillance ◽  
Computational Efficiency ◽  
Nearest Neighbor ◽  
Gait Recognition ◽  
Large Margin ◽  
Extensive Evaluation ◽  
Gait Features ◽  
Gait Feature

Abstract Gait recognition in video surveillance is still challenging because the employed gait features are usually affected by many variations. To overcome this difficulty, this paper presents a novel Deep Large Margin Nearest Neighbor (DLMNN) method for gait recognition. The proposed DLMNN trains a convolutional neural network to project gait feature onto a metric subspace, under which intra-class gait samples are pulled together as small as possible while inter-class samples are pushed apart by a large margin. We provide an extensive evaluation in terms of various scenarios, namely, normal, carrying, clothing, and cross-view condition on two widely used gait datasets. Experimental results demonstrate that the proposed DLMNN achieves competitive gait recognition performances and promising computational efficiency.

scholarly journals Recognition of Brahmi Words by Using Deep Convolutional Neural Network

2020 ◽  
Author(s):  
Neha Gautam ◽  
Soo See Chai ◽  
Jais Jose
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Image Database ◽  
Deep Convolutional Neural Network ◽  
Significant Progress ◽  
Practical Operation ◽  
Series Of Experiments ◽  
Made In

Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the identification of characters, compound characters, and word. This study proposes the use of the deep convolutional neural network with dropout to recognize the Brahmi words. This study also proposed a DCNN for Brahmi word recognition and a series of experiments are performed on standard Brahmi dataset. The practical operation of this method was systematically tested on accessible Brahmi image database, achieving 92.47% recognition rate by CNN with dropout respectively which is among the best while comparing with the ones reported in the literature for the same task.

scholarly journals Computer-Aided Diagnosis Research of a Lung Tumor Based on a Deep Convolutional Neural Network and Global Features

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huiling Lu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gradient Descent ◽  
Lung Tumor ◽  
Recognition Rate ◽  
Computer Aided Diagnosis ◽  
Deep Convolutional Neural Network ◽  
Model Parameters ◽  
Computer Aided ◽  
Aided Diagnosis

Based on the better generalization ability and the feature learning ability of the deep convolutional neural network, it is very significant to use the DCNN on the computer-aided diagnosis of a lung tumor. Firstly, a deep convolutional neural network was constructed according to the fuzzy characteristics and the complexity of lung CT images. Secondly, the relation between model parameters (iterations, different resolution) and recognition rate is discussed. Thirdly, the effects of different model structures for the identification of a lung tumor were analyzed by changing convolution kernel size, feature dimension, and depth of the network. Fourthly, the different optimization methods on how to influence the DCNN performance were discussed from three aspects containing pooling methods (maximum pooling and mean pooling), activation function (sigmoid and ReLU), and training algorithm (batch gradient descent and gradient descent with momentum). Finally, the experimental results verified the feasibility of DCNN used on computer-aided diagnosis of lung tumors, and it can achieve a good recognition rate when selecting the appropriate model parameters and model structure and using the method of gradient descent with momentum.

scholarly journals An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Zhijian Huang ◽  
Bowen Sui ◽  
Jiayi Wen ◽  
Guohe Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Visual Processing ◽  
Recognition Rate ◽  
Deep Convolutional Neural Network ◽  
Fast Method ◽  
Safe Driving ◽  
Port Management ◽  
Video Detection

The shipping industry is developing towards intelligence rapidly. An accurate and fast method for ship image/video detection and classification is of great significance for not only the port management, but also the safe driving of Unmanned Surface Vehicle (USV). Thus, this paper makes a self-built dataset for the ship image/video detection and classification, and its method based on an improved regressive deep convolutional neural network is presented. This method promotes the regressive convolutional neural network from four aspects. First, the feature extraction layer is lightweighted by referring to YOLOv2. Second, a new feature pyramid network layer is designed by improving its structure in YOLOv3. Third, a proper frame and scale suitable for ships are designed with a clustering algorithm to reduced 60% anchors. Last, the activation function is verified and optimized. Then, the detecting experiment on 7 types of ships shows that the proposed method has advantage compared with the YOLO series networks and other intelligent methods. This method can solve the problem of low recognition rate and real-time performance for ship image/video detection and classification with a small dataset. On the testing-set, the final mAP is 0.9209, the Recall is 0.9818, the AIOU is 0.7991, and the FPS is 78–80 in video detection. Thus, this method provides a highly accurate and real-time ship detection method for the intelligent port management and visual processing of the USV. In addition, the proposed regressive deep convolutional network also has a better comprehensive performance than that of YOLOv2/v3.

scholarly journals Computer-Assisted Diagnosis for Diabetic Retinopathy Based on Fundus Images Using Deep Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yung-Hui Li ◽  
Nai-Ning Yeh ◽  
Shih-Jen Chen ◽  
Yu-Chien Chung
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Deep Convolutional Neural Network ◽  
Support Vector ◽  
Svm Classifier ◽  
Computer Assisted ◽  
Fundus Images ◽  
Max Pooling

Diabetic retinopathy (DR) is a complication of long-standing diabetes, which is hard to detect in its early stage because it only shows a few symptoms. Nowadays, the diagnosis of DR usually requires taking digital fundus images, as well as images using optical coherence tomography (OCT). Since OCT equipment is very expensive, it will benefit both the patients and the ophthalmologists if an accurate diagnosis can be made, based solely on reading digital fundus images. In the paper, we present a novel algorithm based on deep convolutional neural network (DCNN). Unlike the traditional DCNN approach, we replace the commonly used max-pooling layers with fractional max-pooling. Two of these DCNNs with a different number of layers are trained to derive more discriminative features for classification. After combining features from metadata of the image and DCNNs, we train a support vector machine (SVM) classifier to learn the underlying boundary of distributions of each class. For the experiments, we used the publicly available DR detection database provided by Kaggle. We used 34,124 training images and 1,000 validation images to build our model and tested with 53,572 testing images. The proposed DR classifier classifies the stages of DR into five categories, labeled with an integer ranging between zero and four. The experimental results show that the proposed method can achieve a recognition rate up to 86.17%, which is higher than previously reported in the literature. In addition to designing a machine learning algorithm, we also develop an app called “Deep Retina.” Equipped with a handheld ophthalmoscope, the average person can take fundus images by themselves and obtain an immediate result, calculated by our algorithm. It is beneficial for home care, remote medical care, and self-examination.

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