Relevant bibliographies by topics / KTH dataset

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'KTH dataset'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "KTH dataset"

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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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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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Dissertations / Theses on the topic "KTH dataset"

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Wu, Shuai-Hui, and 吳帥輝. "Recognizing Human Actions by Optical Flow on the KTH Dataset." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/wt4a7t.

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碩士<br>國立交通大學<br>資訊學院資訊學程<br>105<br>Research in machine perception of human activities has started in the computer vision community since the 1970’s. Recently, recognizing human activities have a large number of applications in the safety surveillance system and care for the elderly and children system. Methods based on local features and spatio-temporal local features are now widely used in human activities recognition. Although they have good recognition accuracy, but they are very complex and time-consuming in feature detection and feature extraction. This thesis proposes a method to represe
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Book chapters on the topic "KTH dataset"

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Gao, Zan, Ming-yu Chen, Alexander G. Hauptmann, and Anni Cai. "Comparing Evaluation Protocols on the KTH Dataset." In Human Behavior Understanding. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14715-9_10.

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Jarvis, Padraigh, Laura Climent, and Alejandro Arbelaez. "Smart and Sustainable Scheduling of Charging Events for Electric Buses." In Springer Proceedings in Political Science and International Relations. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18161-0_8.

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AbstractThe Irish transportation sector currently accounts for more than 30% of the energy related CO2 emissions of the country. Therefore, in order to reach the sustainable goals, the Irish government is working on multiple incentives to promote Electric Vehicles (EV) and infrastructure to decarbonize the sector, e.g., free domestic charging points, tool reductions, and the implementation of electric Buses (eBuses) in the medium to long term. In particular, eBuses operate with rechargeable batteries with a capacity to store approximately 300 kWh (and up to 600 kWh), equivalent to around 29.9
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Lu, Jia, and Wei Qi Yan. "Comparative Evaluations of Human Behavior Recognition Using Deep Learning." In Handbook of Research on Multimedia Cyber Security. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2701-6.ch009.

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With the cost decrease of security monitoring facilities such as cameras, video surveillance has been widely applied to public security and safety such as banks, transportation, shopping malls, etc. which allows police to monitor abnormal events. Through deep learning, authors can achieve high performance of human behavior detection and recognition by using model training and tests. This chapter uses public datasets Weizmann dataset and KTH dataset to train deep learning models. Four deep learning models were investigated for human behavior recognition. Results show that YOLOv3 model is the be
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Saxena, Parul, and R. S. Jadon. "Semantic Tagging of Events in Video Using HNN." In Machine Learning for Societal Improvement, Modernization, and Progress. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4045-2.ch007.

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This chapter describes the semantic tagging of events in videos using an effective combination of machine learning and neural network. Hybrid neural network architecture is proposed to consider the object features generated for each video and combine them with the LSTM model running over the label. The entire system is highly efficient for training and learning as the training dataset is optimized by applying multiple machine learning techniques. Experiments were done on the KTH dataset. Results show that the approach used gives 97% accuracy for the KTH dataset.
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Cózar J.R., Hernández R., Heredia Y., Gonzáalez-Linares J.M.a, and Guil N. "Reducing Vocabulary Size in Human Action Classification." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-105-2-1712.

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Human action classification is an important task in computer vision. Bag-of-Words using spatio-temporal features and some classification algorithm is one of the most successful methods in this context. In this work we have studied the effect of reducing the vocabulary size using a video word ranking method. We have used the KTH dataset to obtain a vocabulary with more descriptive words and, at the same time, more compact and efficient. Results for different vocabulary sizes show an improvement of the recognition rate whilst reducing the number of words due to the fact that non-descriptive word
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Liang, Chenwei, and Wei Qi Yan. "Human Action Recognition Based on YOLOv7." In Deep Learning, Reinforcement Learning, and the Rise of Intelligent Systems. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1738-9.ch006.

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Human action recognition is a fundamental research problem in computer vision. The accuracy of human action recognition has important applications. In this book chapter, the authors use a YOLOv7-based model for human action recognition. To evaluate the performance of the model, the action recognition results of YOLOv7 were compared with those using CNN+LSTM, YOLOv5, and YOLOv4. Furthermore, a small human action dataset suitable for YOLO model training is designed. This data set is composed of images extracted from KTH, Weizmann, MSR data sets. In this book chapter, the authors make use of this
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Tian, YingLi, Liangliang Cao, Zicheng Liu, and Zhengyou Zhang. "Action Detection by Fusing Hierarchically Filtered Motion with Spatiotemporal Interest Point Features." In Human Behavior Recognition Technologies. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3682-8.ch012.

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This chapter addresses the problem of action detection from cluttered videos. In recent years, many feature extraction schemes have been designed to describe various aspects of actions. However, due to the difficulty of action detection, e.g., the cluttered background and potential occlusions, a single type of feature cannot effectively solve the action detection problems in cluttered videos. In this chapter, the authors propose a new type of feature, Hierarchically Filtered Motion (HFM), and further investigate the fusion of HFM with Spatiotemporal Interest Point (STIP) features for action de
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Al-Berry, M. N., Mohammed A. M. Salem, H. M. Ebeid, A. S. Hussein, and Mohamed F. Tolba. "Directional Multi-Scale Stationary Wavelet-Based Representation for Human Action Classification." In Handbook of Research on Machine Learning Innovations and Trends. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2229-4.ch014.

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Human action recognition is a very active field in computer vision. Many important applications depend on accurate human action recognition, which is based on accurate representation of the actions. These applications include surveillance, athletic performance analysis, driver assistance, robotics, and human-centered computing. This chapter presents a thorough review of the field, concentrating the recent action representation methods that use spatio-temporal information. In addition, the authors propose a stationary wavelet-based representation of natural human actions in realistic videos. Th
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Devi, L. Nirmala, and A. Nageswar Rao. "Hybrid Feature Vector-Assisted Action Representation for Human Action Recognition Using Support Vector Machines." In Methodologies and Applications of Computational Statistics for Machine Intelligence. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7701-1.ch001.

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Human action recognition (HAR) is one of most significant research topics, and it has attracted the concentration of many researchers. Automatic HAR system is applied in several fields like visual surveillance, data retrieval, healthcare, etc. Based on this inspiration, in this chapter, the authors propose a new HAR model that considers an image as input and analyses and exposes the action present in it. Under the analysis phase, they implement two different feature extraction methods with the help of rotation invariant Gabor filter and edge adaptive wavelet filter. For every action image, a n
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"Stochastic Interpolation." In Spatial Analysis Techniques Using MyGeoffice®. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3270-5.ch009.

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Spatial analysis includes an expanding array of methods which address different spatial issues, ranging from remote sensing to spatial error uncertainty. Each of these methods focuses on geographically raw data correlated by statistical methods. In general, spatial interpolation and stochastic Kriging, in particular, will be addressed in this chapter. Ordinary Kriging (OK) foundations are presented in the first section which encompasses eight sub-sections (in accordance with the eight myGeoffice© options). Section two introduces Kriging with Trend (KT but sometimes known as Universal Kriging)
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Conference papers on the topic "KTH dataset"

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Shahhoseyni, Shabnam, Arijit Chakraborty, Mohammad Reza Boskabadi, Venkat Venkatasubramanian, and Seyed Soheil Mansouri. "Hybrid machine-learning for dynamic plant-wide biomanufacturing." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.174465.

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This study focuses on biomanufacturing case study, i.e. Lovastatin production, employing a hybrid modeling framework that combines mechanistic and data-driven approaches. A time-series dataset was generated using the KT-Biologics I (KTB1) plantwide model, a dynamic simulation of continuous biomanufacturing. The dataset captures critical parameters such as nutrient concentrations and API production. The AI-DARWIN framework was used to develop interpretable machine learning models with constrained functional forms, ensuring both accuracy and clarity. The resulting polynomial-based models reveal
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Sengupta, Shreeya, Hui Wang, Piyush Ojha, and William Blackburn. "Classification of similar but differently paced activities in the KTH dataset." In Seventh International Conference on Machine Vision (ICMV 2014), edited by Antanas Verikas, Branislav Vuksanovic, Petia Radeva, and Jianhong Zhou. SPIE, 2015. http://dx.doi.org/10.1117/12.2180570.

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Thippur, Akshaya, Rares Ambrus, Gaurav Agrawal, et al. "KTH-3D-TOTAL: A 3D dataset for discovering spatial structures for long-term autonomous learning." In 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV). IEEE, 2014. http://dx.doi.org/10.1109/icarcv.2014.7064543.

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Ranasinghe, Chamila, Hina Noor, and Jeevan Jayasuriya. "A Simplified Method for Determining Gas Turbine Performance Parameters Based Upon Available Catalogue Data." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-25356.

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Overall theoretical performance analysis of gas turbines can be conducted by applying design parameters into several thermodynamic theories and equations. However, limited availability of the design parameters will not provide sufficient room for a detailed analysis. Gas turbine manufacturers publish only a limited amount of design/performance data, while important parameters remained hidden and the available information is not sufficiently enough for obtaining a complete gas turbine performance dataset. Five main parameters commonly provided by a gas turbine manufacturer’s catalogues; pressur
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Tang, Yuzhe, Ling Liu, Junichi Tatemura, and Hakan Hacigumus. "KTV-Tree: Interactive Top-K Aggregation on Dynamic Large Dataset in the Cloud." In 2015 IEEE 35th International Conference on Distributed Computing Systems Workshops (ICDCSW). IEEE, 2015. http://dx.doi.org/10.1109/icdcsw.2015.32.

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Bosch, D., and J. Ledo and P. Queralt. "Fuzzy Logic Based Lithology Determination from Well Log Data - Application to the KTB Project Dataset, Germany." In 1st Sustainable Earth Sciences Conference and Exhibition (SES2011). EAGE Publications BV, 2011. http://dx.doi.org/10.3997/2214-4609.20144196.

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Lakomkin, Egor, Sven Magg, Cornelius Weber, and Stefan Wermter. "KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube Videos." In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/d18-2016.

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Rodrigues, Thales B. S. F., Jairo F. de Souza, Heder S. Bernardino, and Ryan S. Baker. "Towards Interpretability of Attention-Based Knowledge Tracing Models." In Simpósio Brasileiro de Informática na Educação. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbie.2022.224685.

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Knowledge Tracing (KT) models based on attention mechanisms have demonstrated in literature the capability to predict student performance more accurately than previous models in some datasets. However, they fail to directly infer student knowledge. In this paper, we apply a proposed extension already seen in KT literature in order to infer latent knowledge to these models. We apply the extension to four different attention-based KT models, to investigate whether these models can better infer the knowledge outside the learning system than previous models. We find that attention-based models can
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Liu, Yunfei, Yang Yang, Xianyu Chen, Jian Shen, Haifeng Zhang, and Yong Yu. "Improving Knowledge Tracing via Pre-training Question Embeddings." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/219.

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Knowledge tracing (KT) defines the task of predicting whether students can correctly answer questions based on their historical response. Although much research has been devoted to exploiting the question information, plentiful advanced information among questions and skills hasn't been well extracted, making it challenging for previous work to perform adequately. In this paper, we demonstrate that large gains on KT can be realized by pre-training embeddings for each question on abundant side information, followed by training deep KT models on the obtained embeddings. To be specific, the side
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Majidaie, Saeed, Muhammad Hamzi B Yakup, Nurul Nadhira Bt Idris, Ahmad Khanifar, Nor Idah Kechut, and Nourreddine Benayad. "An Advanced Three Phases Allocation Approach in Multi-Stacked Reservoirs with High Complexity on Historical Data During Field Development Plan." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211414-ms.

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Abstract Quality of commingled production data and reliability of back-allocation from stacked reservoirs with numerous platforms, wells, and strings play an important role in reservoir simulation modeling for history matching and prediction. A long historical data, limited surveillance data including routine well tests, pressure, and PLT, relying on conventional back-allocation for high water-cut strings with tubing integrity issues are the common pain points. The pre-HM tool is developed to provide a reliable and clean dataset for modeling. Pre-HM tool includes advanced functionalities such
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