Guest Editors
Dr. Seifedine Kadry, Beirut Arab University, Lebanon.
Dr. Shuihua Wang, University of Leicester, UK.
Dr. V. Rajinikanth, St Joseph’s College, India.
Dr. Muhammad Attique Khan, HITEC University Taxila, Pakistan.
Summary
In the area of computer vision, human action recognition, gait recognition, and gesture recognition (HARGRGR) are important research areas from the last decade. The most important application of HARGRGR is video surveillance. As the imaging technique improvements and the camera expedient promotions, novel approaches for HAR continuously arise. Nowadays, through camera networks, a lot of videos are captured for human activities. Through these activities, it can be possible to predict the future activities of a human. For this purpose, many automated systems are proposed by computer vision researchers using machine learning algorithms. However, the question is how these systems can handle a large number of videos? Also, how they remove redundant or irrelevant information to monitor the required activities? The more recent, deep learning gain a huge success in the area of machine learning to handle a large amount of data with more accuracy as compared to classical techniques. For HARGRGR, deep learning can be more useful because it requires a large amount of data for training.
Sometimes, the deep learning models are trained on complex imaging datasets and due to these complex datasets, the required accuracy cannot be achieved. Therefore, it is possible to fuse two or more than two deep neural networks (layers information, features, etc.). But the question is that how the fusion process impact the system computational time? This problem can be resolve by employing feature reduction techniques.
This special issue aims to gather the achievemen of deep learning, information fusion, and feature selection in fields of action recognition, gait recognition, and gesture recognition.
Keywords
• Human action recognition using deep learning for large video datasets
• Human gait recognition using deep learning
• Human gesture recognition using deep learning
• Deep learning models information fusion for action recognition, gait recognition, and gesture recognition
• Features fusion for action recognition, gait recognition, and gesture recognition
• Features selection and action recognition
• Gesture recognition and features selection
• Gait recognition and features selection
• Gait recognition in the real-time camera network using deep learning
• Learning a deep learning model using body parts for action recognition
Published Papers
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Open Access
ARTICLE
An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic
Maha Farouk S. Sabir, Irfan Mehmood, Wafaa Adnan Alsaggaf, Enas Fawai Khairullah, Samar Alhuraiji, Ahmed S. Alghamdi, Ahmed A. Abd El-Latif
CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4151-4166, 2022, DOI:10.32604/cmc.2022.017865
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Today, due to the pandemic of COVID-19 the entire world is facing a serious health crisis. According to the World Health Organization (WHO), people in public places should wear a face mask to control the rapid transmission of COVID-19. The governmental bodies of different countries imposed that wearing a face mask is compulsory in public places. Therefore, it is very difficult to manually monitor people in overcrowded areas. This research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places, by presenting an automated system that automatically localizes masked and unmasked human…
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Open Access
ARTICLE
Deep Learning-Based Approach for Arabic Visual Speech Recognition
Nadia H. Alsulami, Amani T. Jamal, Lamiaa A. Elrefaei
CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 85-108, 2022, DOI:10.32604/cmc.2022.019450
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Lip-reading technologies are rapidly progressing following the breakthrough of deep learning. It plays a vital role in its many applications, such as: human-machine communication practices or security applications. In this paper, we propose to develop an effective lip-reading recognition model for Arabic visual speech recognition by implementing deep learning algorithms. The Arabic visual datasets that have been collected contains 2400 records of Arabic digits and 960 records of Arabic phrases from 24 native speakers. The primary purpose is to provide a high-performance model in terms of enhancing the preprocessing phase. Firstly, we extract keyframes from our dataset. Secondly, we produce…
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Open Access
ARTICLE
Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks
Muneeb Ur Rehman, Fawad Ahmed, Muhammad Attique Khan, Usman Tariq, Faisal Abdulaziz Alfouzan, Nouf M. Alzahrani, Jawad Ahmad
CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4675-4690, 2022, DOI:10.32604/cmc.2022.019586
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. Many researchers have been working on vision-based gesture recognition due to its various applications. This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation. The 3D-CNN is used for the extraction of spectral and spatial features which are then given to…
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Open Access
ARTICLE
Smart Devices Based Multisensory Approach for Complex Human Activity Recognition
Muhammad Atif Hanif, Tallha Akram, Aamir Shahzad, Muhammad Attique Khan, Usman Tariq, Jung-In Choi, Yunyoung Nam, Zanib Zulfiqar
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3221-3234, 2022, DOI:10.32604/cmc.2022.019815
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Sensors based Human Activity Recognition (HAR) have numerous applications in eHeath, sports, fitness assessments, ambient assisted living (AAL), human-computer interaction and many more. The human physical activity can be monitored by using wearable sensors or external devices. The usage of external devices has disadvantages in terms of cost, hardware installation, storage, computational time and lighting conditions dependencies. Therefore, most of the researchers used smart devices like smart phones, smart bands and watches which contain various sensors like accelerometer, gyroscope, GPS etc., and adequate processing capabilities. For the task of recognition, human activities can be broadly categorized as basic and complex…
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Open Access
ARTICLE
Fast Intra Mode Selection in HEVC Using Statistical Model
Junaid Tariq, Ayman Alfalou, Amir Ijaz, Hashim Ali, Imran Ashraf, Hameedur Rahman, Ammar Armghan, Inzamam Mashood, Saad Rehman
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3903-3918, 2022, DOI:10.32604/cmc.2022.019541
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Comprehension algorithms like High Efficiency Video Coding (HEVC) facilitates fast and efficient handling of multimedia contents. Such algorithms involve various computation modules that help to reduce the size of content but preserve the same subjective viewing quality. However, the brute-force behavior of HEVC is the biggest hurdle in the communication of multimedia content. Therefore, a novel method will be presented here to accelerate the encoding process of HEVC by making early intra mode decisions for the block. Normally, the HEVC applies 35 intra modes to every block of the frame and selects the best among them based on the RD-cost…
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Open Access
ARTICLE
Optimized Convolutional Neural Network Models for Skin Lesion Classification
Juan Pablo Villa-Pulgarin, Anderson Alberto Ruales-Torres, Daniel Arias-Garzón, Mario Alejandro Bravo-Ortiz, Harold Brayan Arteaga-Arteaga, Alejandro Mora-Rubio, Jesus Alejandro Alzate-Grisales, Esteban Mercado-Ruiz, M. Hassaballah, Simon Orozco-Arias, Oscar Cardona-Morales, Reinel Tabares-Soto
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2131-2148, 2022, DOI:10.32604/cmc.2022.019529
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Skin cancer is one of the most severe diseases, and medical imaging is among the main tools for cancer diagnosis. The images provide information on the evolutionary stage, size, and location of tumor lesions. This paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks (CNNs) in distinguishing different skin lesions. The CNNs are based on transfer learning, taking advantage of ImageNet weights. Accordingly, in each experiment, different workflow stages are tested, including data augmentation and fine-tuning optimization. Three CNN models based on DenseNet-201, Inception-ResNet-V2, and…
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Open Access
ARTICLE
Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization
Awais Khan, Muhammad Attique Khan, Muhammad Younus Javed, Majed Alhaisoni, Usman Tariq, Seifedine Kadry, Jung-In Choi, Yunyoung Nam
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2113-2130, 2022, DOI:10.32604/cmc.2022.018270
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Human gait recognition (HGR) has received a lot of attention in the last decade as an alternative biometric technique. The main challenges in gait recognition are the change in in-person view angle and covariant factors. The major covariant factors are walking while carrying a bag and walking while wearing a coat. Deep learning is a new machine learning technique that is gaining popularity. Many techniques for HGR based on deep learning are presented in the literature. The requirement of an efficient framework is always required for correct and quick gait recognition. We proposed a fully automated deep learning and improved…
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Open Access
ARTICLE
Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s
Abdul Hanan Ashraf, Muhammad Imran, Abdulrahman M. Qahtani, Abdulmajeed Alsufyani, Omar Almutiry, Awais Mahmood, Muhammad Attique, Mohamed Habib
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2761-2775, 2022, DOI:10.32604/cmc.2022.018785
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract In recent years, the number of Gun-related incidents has crossed over 250,000 per year and over 85% of the existing 1 billion firearms are in civilian hands, manual monitoring has not proven effective in detecting firearms. which is why an automated weapon detection system is needed. Various automated convolutional neural networks (CNN) weapon detection systems have been proposed in the past to generate good results. However, These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system. These models have a high rate of false negatives because they often fail…
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Open Access
ARTICLE
Anomaly Based Camera Prioritization in Large Scale Surveillance Networks
Altaf Hussain, Khan Muhammad, Hayat Ullah, Amin Ullah, Ali Shariq Imran, Mi Young Lee, Seungmin Rho, Muhammad Sajjad
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2171-2190, 2022, DOI:10.32604/cmc.2022.018181
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Digital surveillance systems are ubiquitous and continuously generate massive amounts of data, and manual monitoring is required in order to recognise human activities in public areas. Intelligent surveillance systems that can automatically ide.pngy normal and abnormal activities are highly desirable, as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring. This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system. The proposed system addresses the limitations of existing manual monitoring surveillance systems using a…
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Open Access
ARTICLE
Towards Prevention of Sportsmen Burnout: Formal Analysis of Sub-Optimal Tournament Scheduling
Syed Rameez Naqvi, Adnan Ahmad, S. M. Riazul Islam, Tallha Akram, M. Abdullah-Al-Wadud, Atif Alamri
CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1509-1526, 2022, DOI:10.32604/cmc.2022.019653
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Scheduling a sports tournament is a complex optimization problem, which requires a large number of hard constraints to satisfy. Despite the availability of several such constraints in the literature, there remains a gap since most of the new sports events pose their own unique set of requirements, and demand novel constraints. Specifically talking of the strictly time bound events, ensuring fairness between the different teams in terms of their rest days, traveling, and the number of successive games they play, becomes a difficult task to resolve, and demands attention. In this work, we present a similar situation with a recently…
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Open Access
ARTICLE
Recognition and Tracking of Objects in a Clustered Remote Scene Environment
Haris Masood, Amad Zafar, Muhammad Umair Ali, Muhammad Attique Khan, Salman Ahmed, Usman Tariq, Byeong-Gwon Kang, Yunyoung Nam
CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1699-1719, 2022, DOI:10.32604/cmc.2022.019572
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Object recognition and tracking are two of the most dynamic research sub-areas that belong to the field of Computer Vision. Computer vision is one of the most active research fields that lies at the intersection of deep learning and machine vision. This paper presents an efficient ensemble algorithm for the recognition and tracking of fixed shape moving objects while accommodating the shift and scale invariances that the object may encounter. The first part uses the Maximum Average Correlation Height (MACH) filter for object recognition and determines the bounding box coordinates. In case the correlation based MACH filter fails, the algorithms…
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Open Access
ARTICLE
Human Gait Recognition: A Deep Learning and Best Feature Selection Framework
Asif Mehmood, Muhammad Attique Khan, Usman Tariq, Chang-Won Jeong, Yunyoung Nam, Reham R. Mostafa, Amira ElZeiny
CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 343-360, 2022, DOI:10.32604/cmc.2022.019250
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Background—Human Gait Recognition (HGR) is an approach based on biometric and is being widely used for surveillance. HGR is adopted by researchers for the past several decades. Several factors are there that affect the system performance such as the walking variation due to clothes, a person carrying some luggage, variations in the view angle.
Proposed—In this work, a new method is introduced to overcome different problems of HGR. A hybrid method is proposed or efficient HGR using deep learning and selection of best features. Four major steps are involved in this work-preprocessing of the video frames, manipulation of the pre-trained…
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Open Access
ARTICLE
Hybrid Evolutionary Algorithm Based Relevance Feedback Approach for Image Retrieval
Awais Mahmood, Muhammad Imran, Aun Irtaza, Qammar Abbas, Habib Dhahri, Esam Mohammed Asem Othman, Arif Jamal Malik, Aaqif Afzaal Abbasi
CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 963-979, 2022, DOI:10.32604/cmc.2022.019291
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Searching images from the large image databases is one of the potential research areas of multimedia research. The most challenging task for nay CBIR system is to capture the high level semantic of user. The researchers of multimedia domain are trying to fix this issue with the help of Relevance Feedback (RF). However existing RF based approaches needs a number of iteration to fulfill user's requirements. This paper proposed a novel methodology to achieve better results in early iteration to reduce the user interaction with the system. In previous research work it is reported that SVM based RF approach generating…
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Open Access
ARTICLE
Multiscale Image Dehazing and Restoration: An Application for Visual Surveillance
Samia Riaz, Muhammad Waqas Anwar, Irfan Riaz, Hyun-Woo Kim, Yunyoung Nam, Muhammad Attique Khan
CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1-17, 2022, DOI:10.32604/cmc.2022.018268
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract The captured outdoor images and videos may appear blurred due to haze, fog, and bad weather conditions. Water droplets or dust particles in the atmosphere cause the light to scatter, resulting in very limited scene discernibility and deterioration in the quality of the image captured. Currently, image dehazing has gained much popularity because of its usability in a wide variety of applications. Various algorithms have been proposed to solve this ill-posed problem. These algorithms provide quite promising results in some cases, but they include undesirable artifacts and noise in haze patches in adverse cases. Some of these techniques take unrealistic…
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Open Access
ARTICLE
Automatic Unusual Activities Recognition Using Deep Learning in Academia
Muhammad Ramzan, Adnan Abid, Shahid Mahmood Awan
CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1829-1844, 2022, DOI:10.32604/cmc.2022.017522
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract In the current era, automatic surveillance has become an active research problem due to its vast real-world applications, particularly for maintaining law and order. A continuous manual monitoring of human activities is a tedious task. The use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few years. Various computer vision techniques have been applied for observation and surveillance of real-world activities. This research study focuses on detecting and recognizing unusual activities in an academic situation such as examination halls, which may help the invigilators observe and restrict the students from cheating or…
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Open Access
ARTICLE
YOLOv2PD: An Efficient Pedestrian Detection Algorithm Using Improved YOLOv2 Model
Chintakindi Balaram Murthy, Mohammad Farukh Hashmi, Ghulam Muhammad, Salman A. AlQahtani
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3015-3031, 2021, DOI:10.32604/cmc.2021.018781
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance. The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases. Therefore, the proposed algorithm YOLOv2 (“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection (referred to as YOLOv2PD) would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes. The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion (MLFF) strategy, which helps to improve the model’s feature extraction ability. In addition, one…
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Open Access
ARTICLE
Multi-Layered Deep Learning Features Fusion for Human Action Recognition
Sadia Kiran, Muhammad Attique Khan, Muhammad Younus Javed, Majed Alhaisoni, Usman Tariq, Yunyoung Nam, Robertas Damaševičius, Muhammad Sharif
CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4061-4075, 2021, DOI:10.32604/cmc.2021.017800
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Human Action Recognition (HAR) is an active research topic in machine learning for the last few decades. Visual surveillance, robotics, and pedestrian detection are the main applications for action recognition. Computer vision researchers have introduced many HAR techniques, but they still face challenges such as redundant features and the cost of computing. In this article, we proposed a new method for the use of deep learning for HAR. In the proposed method, video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer learning. The Resnet-50 Pre-Trained Model is…
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Open Access
ARTICLE
Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning
Muzamil Ahmed, Muhammad Ramzan, Hikmat Ullah Khan, Saqib Iqbal, Muhammad Attique Khan, Jung-In Choi, Yunyoung Nam, Seifedine Kadry
CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2217-2230, 2021, DOI:10.32604/cmc.2021.018103
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Violence recognition is crucial because of its applications in activities related to security and law enforcement. Existing semi-automated systems have issues such as tedious manual surveillances, which causes human errors and makes these systems less effective. Several approaches have been proposed using trajectory-based, non-object-centric, and deep-learning-based methods. Previous studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other methods. However, the their performance must be improved. This study explores the state-of-the-art deep learning architecture of convolutional neural networks (CNNs) and inception V4 to detect and recognize violence using video data. In the…
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Open Access
ARTICLE
Safest Route Detection via Danger Index Calculation and K-Means Clustering
Isha Puthige, Kartikay Bansal, Chahat Bindra, Mahekk Kapur, Dilbag Singh, Vipul Kumar Mishra, Apeksha Aggarwal, Jinhee Lee, Byeong-Gwon Kang, Yunyoung Nam, Reham R. Mostafa
CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2761-2777, 2021, DOI:10.32604/cmc.2021.018128
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract The study aims to formulate a solution for identifying the safest route between any two inputted Geographical locations. Using the New York City dataset, which provides us with location tagged crime statistics; we are implementing different clustering algorithms and analysed the results comparatively to discover the best-suited one. The results unveil the fact that the K-Means algorithm best suits for our needs and delivered the best results. Moreover, a comparative analysis has been performed among various clustering techniques to obtain best results. we compared all the achieved results and using the conclusions we have developed a user-friendly application to provide…
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Open Access
ARTICLE
Visibility Enhancement of Scene Images Degraded by Foggy Weather Condition: An Application to Video Surveillance
Ghulfam Zahra, Muhammad Imran, Abdulrahman M. Qahtani, Abdulmajeed Alsufyani, Omar Almutiry, Awais Mahmood, Fayez Eid Alazemi
CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3465-3481, 2021, DOI:10.32604/cmc.2021.017454
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract In recent years, video surveillance application played a significant role in our daily lives. Images taken during foggy and haze weather conditions for video surveillance application lose their authenticity and hence reduces the visibility. The reason behind visibility enhancement of foggy and haze images is to help numerous computer and machine vision applications such as satellite imagery, object detection, target killing, and surveillance. To remove fog and enhance visibility, a number of visibility enhancement algorithms and methods have been proposed in the past. However, these techniques suffer from several limitations that place strong obstacles to the real world outdoor computer…
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Open Access
ARTICLE
Video Analytics Framework for Human Action Recognition
Muhammad Attique Khan, Majed Alhaisoni, Ammar Armghan, Fayadh Alenezi, Usman Tariq, Yunyoung Nam, Tallha Akram
CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3841-3859, 2021, DOI:10.32604/cmc.2021.016864
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Human action recognition (HAR) is an essential but challenging task for observing human movements. This problem encompasses the observations of variations in human movement and activity identification by machine learning algorithms. This article addresses the challenges in activity recognition by implementing and experimenting an intelligent segmentation, features reduction and selection framework. A novel approach has been introduced for the fusion of segmented frames and multi-level features of interests are extracted. An entropy-skewness based features reduction technique has been implemented and the reduced features are converted into a codebook by serial based fusion. A custom made genetic algorithm is implemented on…
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Open Access
ARTICLE
Convolutional Bi-LSTM Based Human Gait Recognition Using Video Sequences
Javaria Amin, Muhammad Almas Anjum, Muhammad Sharif, Seifedine Kadry, Yunyoung Nam, ShuiHua Wang
CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2693-2709, 2021, DOI:10.32604/cmc.2021.016871
（This article belongs to this Special Issue:
Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
Abstract Recognition of human gait is a difficult assignment, particularly for unobtrusive surveillance in a video and human identification from a large distance. Therefore, a method is proposed for the classification and recognition of different types of human gait. The proposed approach is consisting of two phases. In phase I, the new model is proposed named convolutional bidirectional long short-term memory (Conv-BiLSTM) to classify the video frames of human gait. In this model, features are derived through convolutional neural network (CNN) named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal information. In phase II,…
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