Esam A. AlQaralleh¹, Fahad Aldhaban², Halah Nasseif², Malek Z. Alksasbeh³, Bassam A. Y. Alqaralleh^2,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5593-5605, 2022, DOI:10.32604/cmc.2022.026666

Abstract Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes. The use of deep learning (DL) technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification. The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention. Human action recognition (HAR) is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level. The advancements of the DL models help to accomplish improved recognition performance. In this view,… More >

Norah Saleh Alghamdi¹, Sami Bourouis^2,*, Nizar Bouguila³

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1139-1156, 2022, DOI:10.32604/cmc.2022.022959

Abstract Interest in automated data classification and identification systems has increased over the past years in conjunction with the high demand for artificial intelligence and security applications. In particular, recognizing human activities with accurate results have become a topic of high interest. Although the current tools have reached remarkable successes, it is still a challenging problem due to various uncontrolled environments and conditions. In this paper two statistical frameworks based on nonparametric hierarchical Bayesian models and Gamma distribution are proposed to solve some real-world applications. In particular, two nonparametric hierarchical Bayesian models based on Dirichlet process and Pitman-Yor process are developed.… More >

Motasem S. Alsawadi^*, Miguel Rio

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4643-4658, 2022, DOI:10.32604/cmc.2022.022783

Abstract Action recognition has been recognized as an activity in which individuals’ behaviour can be observed. Assembling profiles of regular activities such as activities of daily living can support identifying trends in the data during critical events. A skeleton representation of the human body has been proven to be effective for this task. The skeletons are presented in graphs form-like. However, the topology of a graph is not structured like Euclidean-based data. Therefore, a new set of methods to perform the convolution operation upon the skeleton graph is proposed. Our proposal is based on the Spatial Temporal-Graph Convolutional Network (ST-GCN) framework.… More >

Jian Zhao¹, Shangwu Chong¹, Liang Huang¹, Xin Li¹, Chen He¹, Jian Jia^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1827-1851, 2022, DOI:10.32604/cmes.2022.017654

Abstract In this paper, we propose an improved deep residual network model to recognize human actions. Action data is composed of channel state information signals, which are continuous fine-grained signals. We replaced the traditional identity connection with the shrinking threshold module. The module automatically adjusts the threshold of the action data signal, and filters out signals that are not related to the principal components. We use the attention mechanism to improve the memory of the network model to the action signal, so as to better recognize the action. To verify the validity of the experiment more accurately, we collected action data… More >

Disha Deotale¹, Madhushi Verma², P. Suresh³, Sunil Kumar Jangir⁴, Manjit Kaur², Sahar Ahmed Idris⁵, Hammam Alshazly^6,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3919-3938, 2022, DOI:10.32604/cmc.2022.020655

Abstract Nowadays, the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data. The video datasets are generated using cameras available in various devices that can be in a static or dynamic position and are referred to as untrimmed videos. Smarter monitoring is a historical necessity in which commonly occurring, regular, and out-of-the-ordinary activities can be automatically identified using intelligence systems and computer vision technology. In a long video, human activity may be present anywhere in the video. There can be a single or multiple human activities present… More >

Sadia Kiran¹, Muhammad Attique Khan¹, Muhammad Younus Javed¹, Majed Alhaisoni², Usman Tariq³, Yunyoung Nam^4,*, Robertas Damaševičius⁵, Muhammad Sharif⁶

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4061-4075, 2021, DOI:10.32604/cmc.2021.017800

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… More >

Muzamil Ahmed^1,2, Muhammad Ramzan^3,4, Hikmat Ullah Khan², Saqib Iqbal⁵, Muhammad Attique Khan⁶, Jung-In Choi⁷, Yunyoung Nam^8,*, Seifedine Kadry⁹

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2217-2230, 2021, DOI:10.32604/cmc.2021.018103

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… More >

Muhammad Attique Khan¹, Majed Alhaisoni², Ammar Armghan³, Fayadh Alenezi³, Usman Tariq⁴, Yunyoung Nam^5,*, Tallha Akram⁶

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3841-3859, 2021, DOI:10.32604/cmc.2021.016864

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… More >

Chao Zhu¹, Yike Wang¹, Dongbing Pu¹,Miao Qi^1,*, Hui Sun^2,*, Lei Tan^3,*

Journal on Big Data, Vol.2, No.3, pp. 95-104, 2020, DOI:10.32604/jbd.2020.010431

Abstract Nowadays, action recognition is widely applied in many fields. However, action is hard to define by single modality information. The difference between image recognition and action recognition is that action recognition needs more modality information to depict one action, such as the appearance, the motion and the dynamic information. Due to the state of action evolves with the change of time, motion information must be considered when representing an action. Most of current methods define an action by spatial information and motion information. There are two key elements of current action recognition methods: spatial information achieved by sampling sparsely on… More >

Shuren Zhou^{1, *}, Le Chen¹, Vijayan Sugumaran²

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1545-1561, 2020, DOI:10.32604/cmc.2020.09867

Abstract The two-stream convolutional neural network exhibits excellent performance in the video action recognition. The crux of the matter is to use the frames already clipped by the videos and the optical flow images pre-extracted by the frames, to train a model each, and to finally integrate the outputs of the two models. Nevertheless, the reliance on the pre-extraction of the optical flow impedes the efficiency of action recognition, and the temporal and the spatial streams are just simply fused at the ends, with one stream failing and the other stream succeeding. We propose a novel hidden twostream collaborative (HTSC) learning… More >