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 - 30 December 2021

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 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 - 27 September 2021

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… 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 - 24 August 2021

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.… 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 - 21 July 2021

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… 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 - 06 May 2021

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 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 - 13 October 2020

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… 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 - 30 April 2020

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 More >

Shiqi Wang¹, Yimin Yang^{1, *}, Ruizhong Wei¹, Qingming Jonathan Wu²

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1081-1091, 2020, DOI:10.32604/cmc.2020.09648 - 30 April 2020

Abstract Human motion recognition plays a crucial role in the video analysis framework. However, a given video may contain a variety of noises, such as an unstable background and redundant actions, that are completely different from the key actions. These noises pose a great challenge to human motion recognition. To solve this problem, we propose a new method based on the 3-Dimensional (3D) Bag of Visual Words (BoVW) framework. Our method includes two parts: The first part is the video action feature extractor, which can identify key actions by analyzing action features. In the video action More >

Binjie Gu^{1, *}, Weili Xiong¹, Zhonghu Bai²

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 243-262, 2020, DOI:10.32604/cmc.2020.06898 - 30 March 2020

Abstract Human action recognition under complex environment is a challenging work. Recently, sparse representation has achieved excellent results of dealing with human action recognition problem under different conditions. The main idea of sparse representation classification is to construct a general classification scheme where the training samples of each class can be considered as the dictionary to express the query class, and the minimal reconstruction error indicates its corresponding class. However, how to learn a discriminative dictionary is still a difficult work. In this work, we make two contributions. First, we build a new and robust human More >

Wei Song^1,2,*, Jing Yu³, Xiaobing Zhao^1,2, Antai Wang⁴

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1189-1204, 2019, DOI:10.32604/cmc.2019.06361

Abstract In the current era of multimedia information, it is increasingly urgent to realize intelligent video action recognition and content analysis. In the past few years, video action recognition, as an important direction in computer vision, has attracted many researchers and made much progress. First, this paper reviews the latest video action recognition methods based on Deep Neural Network and Markov Logic Network. Second, we analyze the characteristics of each method and the performance from the experiment results. Then compare the emphases of these methods and discuss the application scenarios. Finally, we consider and prospect the More >