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Journal articles on the topic 'KTH dataset'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Maheswari, B. Uma, R. Sonia, M. P. Raja Kumar, and J. Ramya. "Novel Machine Learning for Human Actions Classification Using Histogram of Oriented Gradients and Sparse Representation." Information Technology and Control 50, no. 4 (2021): 686–705. http://dx.doi.org/10.5755/j01.itc.50.4.27845.

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Recognition of human actions is a trending research topic as it can be used for crucial medical applications like life care and healthcare. In this research, we propose a novel machine learning algorithm for the classification of human actions based on sparse representation theory. In the proposed framework, the input videos are initially partitioned into several temporal segments of a predefined length. From these temporal segments, the key-cuboids are then obtained. These cuboids are obtained based on the locations having maximum variation in orientation. From these regions, key-cuboids are

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Kamble, Milind, and Rajankumar S. Bichkar. "A Hierarchical Framework for Video-Based Human Activity Recognition Using Body Part Interactions." International journal of electrical and computer engineering systems 14, no. 8 (2023): 881–91. http://dx.doi.org/10.32985/ijeces.14.8.6.

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Human Activity Recognition (HAR) is an important field with diverse applications. However, video-based HAR is challenging because of various factors, such as noise, multiple people, and obscured body parts. Moreover, it is difficult to identify similar activities within and across classes. This study presents a novel approach that utilizes body region relationships as features and a two-level hierarchical model for classification to address these challenges. The proposed system uses a Hidden Markov Model (HMM) at the first level to model human activity, and similar activities are then grouped

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Twumasi-Ankrah, Sampson, Simon Kojo Appiah, Doris Arthur, Wilhemina Adoma Pels, Jonathan Kwaku Afriyie, and Danielson Nartey. "Comparison of outlier detection techniques in non-stationary time series data." Global Journal of Pure and Applied Sciences 27, no. 1 (2021): 55–60. http://dx.doi.org/10.4314/gjpas.v27i1.7.

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This study examined the performance of six outlier detection techniques using a non-stationary time series dataset. Two key issues were of interest. Scenario one was the method that could correctly detect the number of outliers introduced into the dataset whiles scenario two was to find the technique that would over detect the number of outliers introduced into the dataset, when a dataset contains only extreme maxima values, extreme minima values or both. Air passenger dataset was used with different outliers or extreme values ranging from 1 to 10 and 40. The six outlier detection techniques u

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4

Narayana, Ranga, and G. Venkateswara Rao. "A Grey Wolf Intelligence based Recognition of Human-Action in Low Resolution Videos with Minimal Processing Time." International Journal of Communication Networks and Information Security (IJCNIS) 14, no. 1s (2022): 91–99. http://dx.doi.org/10.17762/ijcnis.v14i1s.5597.

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The usage of video cameras for security purposes has grown in recent years. The time for recognition of human plays an important role in solving many real time problems. In this paper, the process for identifying human action is done by separating the background using local binary pattern (LBP) and features extracted using faster histogram of gradients (FHOG) and Eigen values based on power method. The features are combined and optimized using grey wolf optimization (GWO) and finally classified using support vector machine (SVM). The experimental results are compared with existing methods in i

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Vrskova, Roberta, Robert Hudec, Patrik Kamencay, and Peter Sykora. "A New Approach for Abnormal Human Activities Recognition Based on ConvLSTM Architecture." Sensors 22, no. 8 (2022): 2946. http://dx.doi.org/10.3390/s22082946.

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Recognizing various abnormal human activities from video is very challenging. This problem is also greatly influenced by the lack of datasets containing various abnormal human activities. The available datasets contain various human activities, but only a few of them contain non-standard human behavior such as theft, harassment, etc. There are datasets such as KTH that focus on abnormal activities such as sudden behavioral changes, as well as on various changes in interpersonal interactions. The UCF-crime dataset contains categories such as fighting, abuse, explosions, robberies, etc. However,

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Khaliluzzaman, Md, Md. Abu Bakar Siddiq Sayem, and Lutful KaderMisbah. "HActivityNet: A Deep Convolutional Neural Network for Human Activity Recognition." EMITTER International Journal of Engineering Technology 9, no. 2 (2021): 357–76. http://dx.doi.org/10.24003/emitter.v9i2.642.

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Human Activity Recognition (HAR), a vast area of a computer vision research, has gained standings in recent years due to its applications in various fields. As human activity has diversification in action, interaction, and it embraces a large amount of data and powerful computational resources, it is very difficult to recognize human activities from an image. In order to solve the computational cost and vanishing gradient problem, in this work, we have proposed a revised simple convolutional neural network (CNN) model named Human Activity Recognition Network (HActivityNet) that is automaticall

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Chang, Xunyun, and Liangqing Peng. "Visual Sensing Human Motion Detection System for Interactive Music Teaching." Journal of Sensors 2021 (November 19, 2021): 1–10. http://dx.doi.org/10.1155/2021/2311594.

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The purpose is to study the interactive teaching mode of human action recognition technology in music and dance teaching under computer vision. The human action detection and recognition system based on a three-dimensional (3D) convolutional neural network (CNN) is established. Then, a human action recognition model based on the dual channel is proposed on the basis of CNN, and the visual attention mechanism using the interframe differential channel is introduced into the model. Through experiments, the performance of the system in the process of human dance image recognition based on the Kung

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Vidović, Zoran, and Liang Wang. "Objective Posterior Analysis of kth Record Statistics in Gompertz Model." Axioms 14, no. 3 (2025): 152. https://doi.org/10.3390/axioms14030152.

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The Gompertz distribution has proven highly valuable in modeling human mortality rates and assessing the impacts of catastrophic events, such as plagues, financial crashes, and famines. Record data, which capture extreme values and critical trends, are particularly relevant for analyzing such phenomena. In this study, we propose an objective Bayesian framework for estimating the parameters of the Gompertz distribution using record data. We analyze the performance of several objective priors, including the reference prior, Jeffreys’ prior, the maximal data information (MDI) prior, and probabili

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Starczewski, Artur, Magdalena M. Scherer, Wojciech Książek, Maciej Dębski, and Lipo Wang. "A Novel Grid-Based Clustering Algorithm." Journal of Artificial Intelligence and Soft Computing Research 11, no. 4 (2021): 319–30. http://dx.doi.org/10.2478/jaiscr-2021-0019.

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Abstract Data clustering is an important method used to discover naturally occurring structures in datasets. One of the most popular approaches is the grid-based concept of clustering algorithms. This kind of method is characterized by a fast processing time and it can also discover clusters of arbitrary shapes in datasets. These properties allow these methods to be used in many different applications. Researchers have created many versions of the clustering method using the grid-based approach. However, the key issue is the right choice of the number of grid cells. This paper proposes a novel

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Sarma, Moumita Sen, Kaushik Deb, Pranab Kumar Dhar, and Takeshi Koshiba. "Traditional Bangladeshi Sports Video Classification Using Deep Learning Method." Applied Sciences 11, no. 5 (2021): 2149. http://dx.doi.org/10.3390/app11052149.

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Sports activities play a crucial role in preserving our health and mind. Due to the rapid growth of sports video repositories, automatized classification has become essential for easy access and retrieval, content-based recommendations, contextual advertising, etc. Traditional Bangladeshi sport is a genre of sports that bears the cultural significance of Bangladesh. Classification of this genre can act as a catalyst in reviving their lost dignity. In this paper, the Deep Learning method is utilized to classify traditional Bangladeshi sports videos by extracting both the spatial and temporal fe

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11

Azmin, Nurul Shafiqah Hazelin Noor, Muhammad Faiz Pa’suya, Ami Hassan Md Din, Mohamad Azril Che Aziz, and Noorhurul Ain Othman. "Evaluating the Impact of the Recent Combined and Satellite-Only Global Geopotential Model on the Gravimetric Geoid Model." IOP Conference Series: Earth and Environmental Science 1316, no. 1 (2024): 012006. http://dx.doi.org/10.1088/1755-1315/1316/1/012006.

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Abstract Geoid represents Earth’s surface, ocean, and gravitational field, which influence the elevations, shape, and mass distribution of the geopotential surface, a hypothetical surface that is perpendicular to the direction of gravity at every point. This geopotential surface serves as a reference for measuring elevations and is used as a fundamental reference surface for geodetic and surveying purposes. In this study, the Least Squares Modification of Stokes Formula (LSMS) with Additive Corrections (AC), also known as the KTH method, is used to generate a new gravimetric geoid model for Pe

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Lu, Zhiwu, and Yuxin Peng. "Latent Semantic Learning by Efficient Sparse Coding with Hypergraph Regularization." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 411–16. http://dx.doi.org/10.1609/aaai.v25i1.7896.

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This paper presents a novel latent semantic learning algorithm for action recognition. Through efficient sparse coding, we can learn latent semantics (i.e. high-level features) from a large vocabulary of abundant mid-level features (i.e. visual keywords). More importantly, we can capture the manifold structure hidden among mid-level features by incorporating hypergraph regularization into sparse coding. The learnt latent semantics can further be readily used for action recognition by defining a histogram intersection kernel. Different from the traditional latent semantic analysis based on topi

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Chen, Linxuan. "Sustainable Material Cutting Optimization Using Deep Q-Networks: A Reinforcement Learning Approach for Resource Efficiency." ITM Web of Conferences 73 (2025): 01002. https://doi.org/10.1051/itmconf/20257301002.

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This paper proposes an innovative approach to the Cutting Stock Problem (CSP) by integrating Graph Neural Networks (GNN) which effectively extract and process graph-structured data and Deep Reinforcement Learning (DRL) which utilizes the data generated by the GNN model to make sequential cutting decisions. The GNN model is embedded with Graph Convolutional Networks (GCN) layers, while the DRL model is structured with Deep Q-network (DQN). In my own study using KTH-TIPS dataset for model training, I have achieved promising experimental outcomes, decreasing loss functions and stabilizing total r

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14

Latah, Majd. "Human action recognition using support vector machines and 3D convolutional neural networks." International Journal of Advances in Intelligent Informatics 3, no. 1 (2017): 47. http://dx.doi.org/10.26555/ijain.v3i1.89.

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Recently, deep learning approach has been used widely in order to enhance the recognition accuracy with different application areas. In this paper, both of deep convolutional neural networks (CNN) and support vector machines approach were employed in human action recognition task. Firstly, 3D CNN approach was used to extract spatial and temporal features from adjacent video frames. Then, support vector machines approach was used in order to classify each instance based on previously extracted features. Both of the number of CNN layers and the resolution of the input frames were reduced to meet

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15

Tu, Hong-bin, and Li-min Xia. "The Approach for Action Recognition Based on the Reconstructed Phase Spaces." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/495071.

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This paper presents a novel method of human action recognition, which is based on the reconstructed phase space. Firstly, the human body is divided into 15 key points, whose trajectory represents the human body behavior, and the modified particle filter is used to track these key points for self-occlusion. Secondly, we reconstruct the phase spaces for extracting more useful information from human action trajectories. Finally, we apply the semisupervised probability model and Bayes classified method for classification. Experiments are performed on the Weizmann, KTH, UCF sports, and our action d

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Micheal, Olaolu Arowolo, Olubunmi Adebiyi Marion, and Ariyo Adebiyi Ayodele. "A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree." TELKOMNIKA Telecommunication, Computing, Electronics and Control 19, no. 1 (2021): pp. 310~316. https://doi.org/10.12928/TELKOMNIKA.v19i1.16381.

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Malaria larvae accept explosive variable lifecycle as they spread across numerous mosquito vector stratosphere. Transcriptomes arise in thousands of diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene expression that has led to enhanced understanding of genetic queries. RNA seq tests transcript of gene expression, and provides methodological enhancements to machine learning procedures. Researchers have proposed several methods in evaluating and learning biological data. Genetic algorithm (GA) as a feature selection process is used in this study to fetch relevant inform

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Gao, Feng, Sen Li, Yuankang Ye, and Chang Liu. "PMSTD-Net: A Neural Prediction Network for Perceiving Multi-Scale Spatiotemporal Dynamics." Sensors 24, no. 14 (2024): 4467. http://dx.doi.org/10.3390/s24144467.

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With the continuous advancement of sensing technology, applying large amounts of sensor data to practical prediction processes using artificial intelligence methods has become a developmental direction. In sensing images and remote sensing meteorological data, the dynamic changes in the prediction targets relative to their background information often exhibit more significant dynamic characteristics. Previous prediction methods did not specifically analyze and study the dynamic change information of prediction targets at spatiotemporal multi-scale. Therefore, this paper proposes a neural predi

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Nandi, Purab, K. R. Anupama, Himanish Agarwal, Arav Jain, and Siddharth Paliwal. "Use of the k-nearest neighbour and its analysis for fall detection on Systems on a Chip for multiple datasets." Acta IMEKO 12, no. 3 (2023): 1–11. http://dx.doi.org/10.21014/actaimeko.v12i3.1489.

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Fall of an elderly person often leads to serious injuries and death. Many falls occur in the home environment, and hence a reliable fall detection system that can raise alarms with minimum latency is a necessity. Wrist-worn accelerometer-based fall detection systems and multiple datasets are available, but no attempt has been made to analyze the accuracy and precision. Wherever the comparison does exist, it has been run on a cloud. No analysis of the models, convergence, and dataset analysis on Systems on a Chip (SoCs) has ever been attempted. In this paper, we attempt to present why Machine L

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Yang, Wanli, Yimin Chen, Chen Huang, and Mingke Gao. "Video-Based Human Action Recognition Using Spatial Pyramid Pooling and 3D Densely Convolutional Networks." Future Internet 10, no. 12 (2018): 115. http://dx.doi.org/10.3390/fi10120115.

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In recent years, the application of deep neural networks to human behavior recognition has become a hot topic. Although remarkable achievements have been made in the field of image recognition, there are still many problems to be solved in the area of video. It is well known that convolutional neural networks require a fixed size image input, which not only limits the network structure but also affects the recognition accuracy. Although this problem has been solved in the field of images, it has not yet been broken through in the field of video. To address the input problem of fixed size video

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Palaniapan, Sri Ganes, and Ashley Ng Sok Choo. "A Hybrid Model for Human Action Recognition Based on Local Semantic Features." Journal of Advanced Research in Computing and Applications 33, no. 1 (2024): 7–21. http://dx.doi.org/10.37934/arca.33.1.721.

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One of the challenging research in real-time applications like video surveillance, automated surveillance, real-time tracking, and rescue missions is Human Action Recognition (HAR). In HAR complex background of video, illumination, and variations of human actions are domain-challenging issues. Any research can address these issues then only it has a reputation. This domain is complicated by camera settings, viewpoints, and inter-class similarities. Uncontrolled environment challenges have reduced many well-designed models' performance. This paper aims to design an automated human action recogn

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Tu, Hong-bin, Li-min Xia, and Lun-zheng Tan. "Adaptive Self-Occlusion Behavior Recognition Based on pLSA." Journal of Applied Mathematics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/506752.

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Human action recognition is an important area of human action recognition research. Focusing on the problem of self-occlusion in the field of human action recognition, a new adaptive occlusion state behavior recognition approach was presented based on Markov random field and probabilistic Latent Semantic Analysis (pLSA). Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms an occlusion state variable by phase space obtained. Then, we proposed a hierarchical area variety model. Finally, we use the topic model of pLSA to recognize th

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Aigner, S., and M. Körner. "FUTUREGAN: ANTICIPATING THE FUTURE FRAMES OF VIDEO SEQUENCES USING SPATIO-TEMPORAL 3D CONVOLUTIONS IN PROGRESSIVELY GROWING GANS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W16 (September 17, 2019): 3–11. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w16-3-2019.

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<p><strong>Abstract.</strong> We introduce a new <i>encoder-decoder GAN</i> model, <i>FutureGAN</i>, that predicts future frames of a video sequence conditioned on a sequence of past frames. During training, the networks solely receive the raw pixel values as an input, without relying on additional constraints or dataset specific conditions. To capture both the spatial and temporal components of a video sequence, spatio-temporal 3d convolutions are used in all encoder and decoder modules. Further, we utilize concepts of the existing <i>progressiv

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Gao, Zhijun, Qiaoyu Gu, and Zhonghua Han. "Human Behavior Recognition Method based on Two-layer LSTM Network with Attention Mechanism." Journal of Physics: Conference Series 2093, no. 1 (2021): 012006. http://dx.doi.org/10.1088/1742-6596/2093/1/012006.

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Abstract Aiming at the problem that the exiting human skeleton-based action recognition methods cannot fully extract the relevant information before and after the action, resulting in low utilization efficiency of skeleton points, we propose a two-layer LSTM (long short term memory) network with attention mechanism. The network has two layers, the first LSTM network is used for skeleton coding and initialization of system storage units and the second LSTM network integrates attention mechanism to further process the data of the first layer network. An algorithm is designed to assign different

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Kim, Jeongdae, Hyunseung Choo, and Jongpil Jeong. "Self-Attention (SA)-ConvLSTM Encoder–Decoder Structure-Based Video Prediction for Dynamic Motion Estimation." Applied Sciences 14, no. 23 (2024): 11315. https://doi.org/10.3390/app142311315.

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Video prediction, which is the task of predicting future video frames based on past observations, remains a challenging problem because of the complexity and high dimensionality of spatiotemporal dynamics. To address the problems associated with spatiotemporal prediction, which is an important decision-making tool in various fields, several deep learning models have been proposed. Convolutional long short-term memory (ConvLSTM) can capture space and time simultaneously and has shown excellent performance in various applications, such as image and video prediction, object detection, and semanti

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Gide, Aisha Ibrahim, and Abubakar Aminu Mu’azu. "A Real-Time Intrusion Detection System for DoS/DDoS Attack Classification in IoT Networks Using KNN-Neural Network Hybrid Technique." Babylonian Journal of Internet of Things 2024 (July 5, 2024): 60–69. http://dx.doi.org/10.58496/bjiot/2024/008.

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As more devices are connected to the Internet through the Internet of Things (IoT), there are huge security challenges. One of the major problems is Distributed Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. These attacks floods networks with useless traffic, disrupting IoT services. There is a need for better security measures to handle this. Intrusion Detection Systems (IDS) is used to find suspicious activities, but many of them can't keep up with new types of attacks in real time. This study focuses on creating an efficient real time hybrid framework that uses th

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Lin, Zhihui, Maomao Li, Zhuobin Zheng, Yangyang Cheng, and Chun Yuan. "Self-Attention ConvLSTM for Spatiotemporal Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11531–38. http://dx.doi.org/10.1609/aaai.v34i07.6819.

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Spatiotemporal prediction is challenging due to the complex dynamic motion and appearance changes. Existing work concentrates on embedding additional cells into the standard ConvLSTM to memorize spatial appearances during the prediction. These models always rely on the convolution layers to capture the spatial dependence, which are local and inefficient. However, long-range spatial dependencies are significant for spatial applications. To extract spatial features with both global and local dependencies, we introduce the self-attention mechanism into ConvLSTM. Specifically, a novel self-attenti

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Wang, Qiulin, Baole Tao, Fulei Han, and Wenting Wei. "Extraction and Recognition Method of Basketball Players’ Dynamic Human Actions Based on Deep Learning." Mobile Information Systems 2021 (June 26, 2021): 1–6. http://dx.doi.org/10.1155/2021/4437146.

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The extraction and recognition of human actions has always been a research hotspot in the field of state recognition. It has a wide range of application prospects in many fields. In sports, it can reduce the occurrence of accidental injuries and improve the training level of basketball players. How to extract effective features from the dynamic body movements of basketball players is of great significance. In order to improve the fairness of the basketball game, realize the accurate recognition of the athletes’ movements, and simultaneously improve the level of the athletes and regulate the mo

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Alsaedi, Malik A., Abdulrahman Saeed Mohialdeen, and Baraa Munqith Albaker. "Development of 3D convolutional neural network to recognize human activities using moderate computation machine." Bulletin of Electrical Engineering and Informatics 10, no. 6 (2021): 3137–46. http://dx.doi.org/10.11591/eei.v10i6.2802.

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Human activity recognition (HAR) is recently used in numerous applications including smart homes to monitor human behavior, automate homes according to human activities, entertainment, falling detection, violence detection, and people care. Vision-based recognition is the most powerful method widely used in HAR systems implementation due to its characteristics in recognizing complex human activities. This paper addresses the design of a 3D convolutional neural network (3D-CNN) model that can be used in smart homes to identify several numbers of activities. The model is trained using KTH datase

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Sowmiya, D., and P. Anandhakumar. "Human Detection and Segmentation Using Automatic Geodesic Active Contours for Vision Based Activity Recognition Applications." Journal of Computational and Theoretical Nanoscience 15, no. 2 (2018): 409–16. http://dx.doi.org/10.1166/jctn.2018.7103.

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Vision-based activity recognition applications are in need of Automatic human detection and segmentation for surveillance purposes. Though there are many state-of-art methods in the literature, there still exist many challenges such as self-occlusion, illumination variations and sensitive to light conditions, appearance, and variations due to clothing. In this paper, a new novel framework for automatic detection and segmentation of the human region in a video sequence using Automatic Geodesic Active Contours is proposed. Normally geodesic active contours have static Region of coincidence but i

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Alhakbani, Noura, Maha Alghamdi, and Abeer Al-Nafjan. "Design and Development of an Imitation Detection System for Human Action Recognition Using Deep Learning." Sensors 23, no. 24 (2023): 9889. http://dx.doi.org/10.3390/s23249889.

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Human action recognition (HAR) is a rapidly growing field with numerous applications in various domains. HAR involves the development of algorithms and techniques to automatically identify and classify human actions from video data. Accurate recognition of human actions has significant implications in fields such as surveillance and sports analysis and in the health care domain. This paper presents a study on the design and development of an imitation detection system using an HAR algorithm based on deep learning. This study explores the use of deep learning models, such as a single-frame conv

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Malik, A. Alsaedi, S. Mohialdeen Abdulrahman, and M. Albaker Baraa. "Development of 3D convolutional neural network to recognize human activities using moderate computation machine." Bulletin of Electrical Engineering and Informatics 10, no. 6 (2021): 3137–46. https://doi.org/10.11591/eei.v10i6.2802.

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Human activity recognition (HAR) is recently used in numerous applications including smart homes to monitor human behavior, automate homes according to human activities, entertainment, falling detection, violence detection, and people care. Vision-based recognition is the most powerful method widely used in HAR systems implementation due to its characteristics in recognizing complex human activities. This paper addresses the design of a 3D convolutional neural network (3D-CNN) model that can be used in smart homes to identify several numbers of activities. The model is trained using KTH datase

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Yan, Xuebo, and Yuemin Fan. "Foreground Extraction and Motion Recognition Technology for Intelligent Video Surveillance." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 10 (2020): 2055021. http://dx.doi.org/10.1142/s0218001420550216.

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With the rapid development of computer technology and network technology, it has become possible to build a large-scale networked video surveillance system. The video surveillance system has become a new type of infrastructure necessary for modern cities. In this paper, the problem of foreground extraction and motion recognition in intelligent video surveillance is studied. The three key sub-problems, namely the extraction of motion foreground in video, the deblurring of motion foreground and the recognition of human motion, are studied and corresponding solutions are proposed. A background mo

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Li, Zhiwei, Anyu Zhang, Fangfang Han, Junchao Zhu, and Yawen Wang. "Worker Abnormal Behavior Recognition Based on Spatio-Temporal Graph Convolution and Attention Model." Electronics 12, no. 13 (2023): 2915. http://dx.doi.org/10.3390/electronics12132915.

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In response to the problem where many existing research models only consider acquiring the temporal information between sequences of continuous skeletons and in response to the lack of the ability to model spatial information, this study proposes a model for recognizing worker falls and lays out abnormal behaviors based on human skeletal key points and a spatio-temporal graph convolutional network (ST-GCN). Skeleton extraction of the human body in video sequences was performed using Alphapose. To resolve the problem of graph convolutional networks not being effective enough for skeletal key po

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Chen, Dong, Tao Zhang, Peng Zhou, Chenyang Yan, and Chuanqi Li. "OFPI: Optical Flow Pose Image for Action Recognition." Mathematics 11, no. 6 (2023): 1451. http://dx.doi.org/10.3390/math11061451.

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Most approaches to action recognition based on pseudo-images involve encoding skeletal data into RGB-like image representations. This approach cannot fully exploit the kinematic features and structural information of human poses, and convolutional neural network (CNN) models that process pseudo-images lack a global field of view and cannot completely extract action features from pseudo-images. In this paper, we propose a novel pose-based action representation method called Optical Flow Pose Image (OFPI) in order to fully capitalize on the spatial and temporal information of skeletal data. Spec

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Ranganarayana, Katakam, and Gurrala Venkateswara Rao. "Modified Ant Colony Optimization for Human Recognition in Videos of Low Resolution." Revue d'Intelligence Artificielle 36, no. 5 (2022): 731–36. http://dx.doi.org/10.18280/ria.360510.

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Privacy protections for people filmed in public settings is a prerequisite to widespread camera use. For this reason, low-resolution videos are used from which specific people can be reliably obscured. Since the human region in low-resolution videos comprises of so few pixels and so little information, human detection is more challenging there than it is in high-resolution videos. With the current state of affairs, one of the most important challenges is tracking a target from lower resolution movies. Identification or monitoring of persons in low-resolution movies has become a common issue in

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Patel, Chirag I., Dileep Labana, Sharnil Pandya, Kirit Modi, Hemant Ghayvat, and Muhammad Awais. "Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences." Sensors 20, no. 24 (2020): 7299. http://dx.doi.org/10.3390/s20247299.

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Human Action Recognition (HAR) is the classification of an action performed by a human. The goal of this study was to recognize human actions in action video sequences. We present a novel feature descriptor for HAR that involves multiple features and combining them using fusion technique. The major focus of the feature descriptor is to exploits the action dissimilarities. The key contribution of the proposed approach is to built robust features descriptor that can work for underlying video sequences and various classification models. To achieve the objective of the proposed work, HAR has been

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Fan, Kun, Chungin Joung, and Seungjun Baek. "Sequence-to-Sequence Video Prediction by Learning Hierarchical Representations." Applied Sciences 10, no. 22 (2020): 8288. http://dx.doi.org/10.3390/app10228288.

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Video prediction which maps a sequence of past video frames into realistic future video frames is a challenging task because it is difficult to generate realistic frames and model the coherent relationship between consecutive video frames. In this paper, we propose a hierarchical sequence-to-sequence prediction approach to address this challenge. We present an end-to-end trainable architecture in which the frame generator automatically encodes input frames into different levels of latent Convolutional Neural Network (CNN) features, and then recursively generates future frames conditioned on th

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Tu, Hong-bin, Li-min Xia, and Zheng-wu Wang. "The Complex Action Recognition via the Correlated Topic Model." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/810185.

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Human complex action recognition is an important research area of the action recognition. Among various obstacles to human complex action recognition, one of the most challenging is to deal with self-occlusion, where one body part occludes another one. This paper presents a new method of human complex action recognition, which is based on optical flow and correlated topic model (CTM). Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms of an occlusion state variable. Secondly, the structure from motion (SFM) is used for reconstruc

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Genc, Erdal, Mustafa Eren Yildirim, and Yucel Batu Salman. "Human activity recognition with fine-tuned CNN-LSTM." Journal of Electrical Engineering 75, no. 1 (2024): 8–13. http://dx.doi.org/10.2478/jee-2024-0002.

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Abstract Human activity recognition (HAR) by deep learning is a challenging and interesting topic. Although there are robust models, there is also a bunch of parameters and variables, which affect the performance such as the number of layers, pooling type. This study presents a new deep learning architecture that is obtained by fine-tuning of the conventional CNN-LSTM model, namely, CNN (+3)-LSTM. Three changes are made to the conventional model to increase the accuracy. Firstly, kernel size is set to 1×1 to extract more information. Secondly, three convolutional layers are added to the model.

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Mizher, Manar Abduljabbar, Mei Choo Ang, and Ahmad Abdel Jabbar Mazhar. "A Meaningful Compact Key Frames Extraction in Complex Video Shots." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 3 (2017): 818. http://dx.doi.org/10.11591/ijeecs.v7.i3.pp818-829.

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Key frame extraction is an essential technique in the computer vision field. The extracted key frames should brief the salient events with an excellent feasibility, great efficiency, and with a high-level of robustness. Thus, it is not an easy problem to solve because it is attributed to many visual features. This paper intends to solve this problem by investigating the relationship between these features detection and the accuracy of key frames extraction techniques using TRIZ. An improved algorithm for key frame extraction was then proposed based on an accumulative optical flow with a self-a

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Paramasivam, Kalaivani, Mohamed Mansoor Roomi Sindha, and Sathya Bama Balakrishnan. "KNN-Based Machine Learning Classifier Used on Deep Learned Spatial Motion Features for Human Action Recognition." Entropy 25, no. 6 (2023): 844. http://dx.doi.org/10.3390/e25060844.

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Human action recognition is an essential process in surveillance video analysis, which is used to understand the behavior of people to ensure safety. Most of the existing methods for HAR use computationally heavy networks such as 3D CNN and two-stream networks. To alleviate the challenges in the implementation and training of 3D deep learning networks, which have more parameters, a customized lightweight directed acyclic graph-based residual 2D CNN with fewer parameters was designed from scratch and named HARNet. A novel pipeline for the construction of spatial motion data from raw video input

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Manar, A. Mizher, Choo Ang Mei, and A. Mazhar Ahmad. "A meaningful Compact Key Frames Extraction in Complex Video Shots." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 1 (2017): 818–29. https://doi.org/10.11591/ijeecs.v7.i3.pp818-829.

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Key frame extraction is an essential technique in the computer vision field. The extracted key frames should brief the salient events with an excellent feasibility, great efficiency, and with a high-level of robustness. Thus, it is not an easy problem to solve because it is attributed to many visual features. This paper intends to solve this problem by investigating the relationship between these features detection and the accuracy of key frames extraction techniques using TRIZ. An improved algorithm for key frame extraction was then proposed based on an accumulative optical flow with a self-a

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Liang, Siyuan. "Optimizing Waste Management Systems through Image Recognition and Feature Extraction Techniques Integrating DQN-Based Models." ITM Web of Conferences 73 (2025): 01001. https://doi.org/10.1051/itmconf/20257301001.

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With increasing global attention on environmental protection and carbon neutrality goals, waste management has become a crucial component of achieving sustainability. Traditional waste disposal methods, such as manual sorting, identification, and crushing, often suffer from low efficiency and inconsistency. This study proposes a waste management system that integrates Artificial Intelligence (AI) to enhance the accuracy and efficiency of waste treatment through automation. The proposed system addresses waste classification, recognition, and fragmentation, utilizing advanced AI technologies lik

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Ren, Yanzhao, Jinyuan Ye, Xiaochuan Wang, Fengjin Xiao, and Ruijun Liu. "SAM-Net: Spatio-Temporal Sequence Typhoon Cloud Image Prediction Net with Self-Attention Memory." Remote Sensing 16, no. 22 (2024): 4213. http://dx.doi.org/10.3390/rs16224213.

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Cloud image prediction is a spatio-temporal sequence prediction task, similar to video prediction. Spatio-temporal sequence prediction involves learning from historical data and using the learned features to generate future images. In this process, the changes in time and space are crucial for spatio-temporal sequence prediction models. However, most models now rely on stacking convolutional layers to obtain local spatial features. In response to the complex changes in cloud position and shape in cloud images, the prediction module of the model needs to be able to extract both global and local

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N, Pavithra, and H. Sharath Kumar Y. "A Computational Meta-Learning Inspired Model for Sketch-based Video Retrieval." Indian Journal of Science and Technology 16, no. 7 (2023): 476–84. https://doi.org/10.17485/IJST/v16i7.2121.

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ABSTRACT <strong>Objectives:</strong> To design and develop an efficient computing framework for sketch-based video retrieval using fine-grained intrinsic computational approach. <strong>Methods:</strong> The primary method of sketch-based video retrieval adopts multi-stream multi-modality of joint embedding method for improved P-SBVR from improved fine-grained KTH and TSF related dataset. It considers the potential aspects of the computation of significant visual intrinsic appearance details for sketch objects. The extracted appearance and motion-based features are used to trai

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Ibrahim Gide, Aisha, and Abubakar Aminu Mu'azu. "NOVEL APPROACH FOR ADDRESSING IOT NETWORKS VULNERABILITIES IN DETECTION AND CLASSIFICATION OF DOS/DDOS ATTACKS." International Journal of Software Engineering and Computer Systems 10, no. 1 (2024): 50–59. http://dx.doi.org/10.15282/ijsecs.10.1.2024.5.0123.

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The substantial growth of Internet-connected devices within the Internet of Things (IoT) has given rise to significant security challenges. Among the various threats confronting these interconnected devices, Denial of Service (DoS)/Distributed Denial of Service (DDoS) attacks emerge as significant concerns. The attacks, which seek to disrupt IoT services by flooding networks with unnecessary traffic, there is a critical need for robust security measures. Intrusion Detection Systems (IDS) are vital in identifying suspicious activities, yet many existing systems lack real-time capabilities to ad

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Wang, Yangyang, Yibo Li, and Xiaofei Ji. "Human Action Recognition Based on Normalized Interest Points and Super-Interest Points." International Journal of Humanoid Robotics 11, no. 01 (2014): 1450005. http://dx.doi.org/10.1142/s0219843614500054.

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Visual-based human action recognition is currently one of the most active research topics in computer vision. The feature representation directly has a crucial impact on the performance of the recognition. Feature representation based on bag-of-words is popular in current research, but the spatial and temporal relationship among these features is usually discarded. In order to solve this issue, a novel feature representation based on normalized interest points is proposed and utilized to recognize the human actions. The novel representation is called super-interest point. The novelty of the pr

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Memon, Fayaz Ahmed, Majid Hussain Memon, Imtiaz Ali Halepoto, Rafia Memon, and Ali Raza Bhangwar. "Action Recognition in videos using VGG19 pre-trained based CNN-RNN Deep Learning Model." VFAST Transactions on Software Engineering 12, no. 1 (2024): 46–57. http://dx.doi.org/10.21015/vtse.v12i1.1711.

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Automatic identification and classification of human actions is one the important and challenging tasks in the field of computer vision that has appealed many researchers since last two decays. It has wide range of applications such as security and surveillance, sports analysis, video analysis, human computer interaction, health care, autonomous vehicles and robotic. In this paper we developed and trained a VGG19 based CNN-RNN deep learning model using transfer learning for classification or prediction of actions and its performance is evaluated on two public actions datasets; KTH and UCF11. T

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Khater, Sarah, Magda B. Fayek, and Mayada Hadhoud. "A Novel GA-based Approach to Automatically Generate ConvLSTM Architectures for Human Activity Recognition." JUCS - Journal of Universal Computer Science 31, no. 5 (2025): 494–518. https://doi.org/10.3897/jucs.131543.

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Human activity recognition (HAR) is a challenging computer vision problem that requires recognizing and categorizing human actions using spatiotemporal data. In recent years, ConvLSTM has shown distinctive advances in manipulating spatiotemporal data. ConvLSTM-based architectures, as any deep learning architecture, require deciding on many hyperparameters apart from trainable weights. State-of-the-art designs for general purpose datasets already exist, but specific purpose applications require architecture designs that perform well on application-dependent datasets. The design of such architec

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Khater, Sarah, Magda B. Fayek, and Mayada Hadhoud. "A Novel GA-based Approach to Automatically Generate ConvLSTM Architectures for Human Activity Recognition." JUCS - Journal of Universal Computer Science 31, no. (5) (2025): 494–518. https://doi.org/10.3897/jucs.131543.

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Human activity recognition (HAR) is a challenging computer vision problem that requires recognizing and categorizing human actions using spatiotemporal data. In recent years, ConvLSTM has shown distinctive advances in manipulating spatiotemporal data. ConvLSTM-based architectures, as any deep learning architecture, require deciding on many hyperparameters apart from trainable weights. State-of-the-art designs for general purpose datasets already exist, but specific purpose applications require architecture designs that perform well on application-dependent datasets. The design of such architec

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