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Journal articles on the topic 'Human action recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Labana, Dileep, and Kirit Modi. "Human Action Recognition Using Dense Trajectories." Indian Journal Of Science And Technology 16, no. 43 (2023): 3846–53. http://dx.doi.org/10.17485/ijst/v16i43.2408.

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2

Abdelrazik, Mostafa A., Abdelhaliem Zekry, and Wael A. Mohamed. "Efficient Hybrid Algorithm for Human Action Recognition." Journal of Image and Graphics 11, no. 1 (2023): 72–81. http://dx.doi.org/10.18178/joig.11.1.72-81.

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Recently, researchers have sought to find the ideal way to recognize human actions through video using artificial intelligence due to the multiplicity of applications that rely on it in many fields. In general, the methods have been divided into traditional methods and deep learning methods, which have provided a qualitative leap in the field of computer vision. Convolutional neural network CNN and recurrent neural network RNN are the most popular algorithms used with images and video. The researchers combined the two algorithms to search for the best results in a lot of research. In an attemp

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Raju, Md Ismail Hossain, Sharmeen Sultana Ananna, Syed Shafiul Islam Meraz, Md Zakaria Azam, Seiichi Serikawa, and Md Atiqur Rahman Ahad. "Human Action Recognition: A Template Matching-based Approach." Journal of the Institute of Industrial Applications Engineers 5, no. 1 (2017): 15–23. http://dx.doi.org/10.12792/jiiae.5.15.

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Mori, Taketoshi, and Kousuke Tsujioka. "Human-Like Daily Action Recognition Model." Journal of Robotics and Mechatronics 17, no. 6 (2005): 672–80. http://dx.doi.org/10.20965/jrm.2005.p0672.

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This paper proposes a human-like action recognition model. When the model is implemented as a system, the system recognizes human actions similarly to human beings recognize. The recognition algorithm is constructed taking account of the following characteristics of human action recognition: simultaneous recognition, priority between actions, judgement fuzziness, multiple judge conditions for one action, and recognition ability from partial view of the body. The experiments based on a comparison with completed questionnaires demonstrated that the system recognizes human action the way like a h

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Mansouri, Amine, Toufik Bakir, and Smain Femmam. "Human Action Recognition with Skeleton and Infrared Fusion Model." Journal of Image and Graphics 11, no. 4 (2023): 309–20. http://dx.doi.org/10.18178/joig.11.4.309-320.

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Skeleton-based human action recognition conveys interesting information about the dynamics of a human body. In this work, we develop a method that uses a multi-stream model with connections between the parallel streams. This work is inspired by a state-of-the-art method called FUSIONCPA that merges different modalities: infrared input and skeleton input. Because we are interested in investigating improvements related to the skeleton-branch backbone, we used the Spatial-Temporal Graph Convolutional Networks (ST-GCN) model and an EfficientGCN attention module. We aim to provide improvements when

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Dhivya, Karunya S., and Kumar Krishna. "Human Activity Recognition Methods." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 1024–28. https://doi.org/10.35940/ijeat.E9771.069520.

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Human action in a video based application plays a significant role that alerts the researchers towards recognizing the motion of human. Other video applications also have video content extraction, summarization, and human computer interactions. The existing methods needs manual footnote of pertinent portion of actions of our interest. Recognition of human action can be done authentic without physical commentary of applicable parts of action of any one’s interest. In this paper we try to update the previous reviews on many ways of recognizing Human activities in videos that had different

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Et. al., Mihir Verma,. "Action Recognition Using Deep Learning And Cnn." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 11 (2021): 818–24. http://dx.doi.org/10.17762/turcomat.v12i11.5967.

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Automated action recognition using Deep learning and CNN is playing a vital role in today‘s day to day society, it may be video action recognitions through cctv, or it may be the smart homes. Now day’s human actions are used in many devices to control them like HoloLens VR, for that recognition of action is important that why video recognition. This Paper represents practical, reliable, and generic systems for video-based human action recognition, technology of CNN network is used to recognize different layers of the video images features. These features are obtained by extracting the features

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Pandey, Ritik, Yadnesh Chikhale, Ritik Verma, and Deepali Patil. "Deep Learning based Human Action Recognition." ITM Web of Conferences 40 (2021): 03014. http://dx.doi.org/10.1051/itmconf/20214003014.

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Human action recognition has become an important research area in the fields of computer vision, image processing, and human-machine or human-object interaction due to its large number of real time applications. Action recognition is the identification of different actions from video clips (an arrangement of 2D frames) where the action may be performed in the video. This is a general construction of image classification tasks to multiple frames and then collecting the predictions from each frame. Different approaches are proposed in literature to improve the accuracy in recognition. In this pa

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9

Liu, Lijue, Xiaoliang Lei, Baifan Chen, and Lei Shu. "Human Action Recognition Based on Inertial Sensors and Complexity Classification." Journal of Information Technology Research 12, no. 1 (2019): 18–35. http://dx.doi.org/10.4018/jitr.2019010102.

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In this article, a human action recognition technique based on complexity classification is proposed. Considering the features of human actions such as continuity, individuality, variety randomness, the demands for recognition of different types of actions are different, the problem of action recognition can be classified into simple action recognition and complex action recognition -- the classification criterions are given respectively. Meanwhile, the hardware design of data acquisition device is introduced and the angle variation is chosen to represent the user's body state changes. For sim

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10

Gaikwad, Suhani, Rutuja Ghodekar, Nikhil Gatkal, and Atharv Prayag. "Human Action Recognition using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1888–92. http://dx.doi.org/10.22214/ijraset.2023.51960.

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Abstract: The aim of this project is to recognize human actions for monitoring and security purposes. This project is mainly focused on building a system that is helpful for doctors to monitor patients .Human Action Recognition is required to recognize a set of human activities by training a supervised learning model and displaying the activity/action result as per the input action received. It has wide range of applications such as patient monitoring system, ATM/ Bank security system, etc. Human Action Recognition model can be mainly used for security and monitoring purposes. We can use vario

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11

Zheng, Ying, Hongxun Yao, Xiaoshuai Sun, Sicheng Zhao, and Fatih Porikli. "Distinctive action sketch for human action recognition." Signal Processing 144 (March 2018): 323–32. http://dx.doi.org/10.1016/j.sigpro.2017.10.022.

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12

Jiang, Liubing, Minyang Wu, Li Che, Xiaoyong Xu, Yujie Mu, and Yongman Wu. "Continuous Human Motion Recognition Based on FMCW Radar and Transformer." Journal of Sensors 2023 (January 24, 2023): 1–14. http://dx.doi.org/10.1155/2023/2951812.

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Radar-based human motion recognition has received extensive attention in recent years. Most current recognition methods generate a heat map of features through simple signal processing and then feed into a classification-based neural network for recognition. Such an approach can only identify a single action. When a set of data contains information about multiple movements, it can also only be recognized as a single movement. Another point that cannot be overlooked is that continuous action recognition methods are able to recognize continuously changing actions but ignore the issue of whether

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13

K, Abhijat Krishna, Milen Eldo, Jinzen Kuriakose, Abin JS, Suzen Saju Kallungal, and Rotney Roy Meckamalil. "Human Action Recognition System Using LRCN." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 1766–74. http://dx.doi.org/10.22214/ijraset.2024.61923.

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Abstract: The importance of human action recognition is significant across various domains, driving advancements in safety, healthcare, sports analytics, and interactive technologies. Leveraging machine learning techniques like Long Recurrent Convolutional Neural Networks (LRCN) trained on datasets such as UCF50, our project focuses on action prediction from YouTube videos. This technology plays a pivotal role in enhancingsafety and security by enabling the detection of anomalies and suspicious behaviours in surveillance systems. In healthcare, it supports remote patient monitoring, rehabilita

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14

Tolety, Kavya. "Human Action Recognition in Videos." International Journal for Research in Applied Science and Engineering Technology 8, no. 8 (2020): 759–63. http://dx.doi.org/10.22214/ijraset.2020.31009.

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15

Wei Bian, Dacheng Tao, and Yong Rui. "Cross-Domain Human Action Recognition." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42, no. 2 (2012): 298–307. http://dx.doi.org/10.1109/tsmcb.2011.2166761.

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16

Hou, Xiangfeng, and Qing Ji. "Research on the Recognition Algorithm of Basketball Technical Action Based on BP Neural System." Scientific Programming 2022 (January 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/7668425.

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Vision-based intelligent human action recognition is the most challenging direction in the field of computer vision in recent years. It detects human actions in video sequences, extracts action features and learns action features, and then recognizes human actions in videos. This paper is based on BP neural network’s basketball technique action recognition and experimental verification. First, design a basketball technique action recognition method based on BP neural network, analyze basketball actions, collect relevant test data, and divide the methods of basketball action recognition. Finall

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17

Subbareddy, K. Venkata, B. Pavani Pavani, G. Sowmya, and N. Ramadevi. "Residual Neural Networks for Human Action Recognition from RGB-D Videos." Journal of Image and Graphics 11, no. 4 (2023): 343–52. http://dx.doi.org/10.18178/joig.11.4.343-352.

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Recently, the RGB-D based Human Action Recognition (HAR) has gained significant research attention due to the provision of complimentary information by different data modalities. However, the current models have experienced still unsatisfactory results due to several problems including noises and view point variations between different actions. To sort out these problems, this paper proposes two new action descriptors namely Modified Depth Motion Map (MDMM) and Spherical Redundant Joint Descriptor (SRJD). MDMM eliminates the noises from depth maps and preserves only the action related informat

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18

Liu, Dan, Mao Ye, and Jianwei Zhang. "Improving Action Recognition Using Sequence Prediction Learning." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (2020): 2050029. http://dx.doi.org/10.1142/s0218001420500299.

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Skeleton-based action recognition distinguishes human actions using the trajectories of skeleton joints, which can be a good representation of human behaviors. Conventional methods usually construct classifiers with hand-crafted or the learned features to recognize human actions. Different from constructing a direct action classifier for action recognition task, this paper attempts to identify human actions based on the development trends of behavior sequences. Specifically, we first utilize the memory neural network to construct action predictors for each kind of activity. These action predic

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19

Zhu, Shao Ping. "Human Action Recognition Based on Improved MIL." Applied Mechanics and Materials 713-715 (January 2015): 2152–55. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2152.

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According to the problem that achieves robust human actions recognition from image sequences in computer vision, using the Iterative Querying Heuristic algorithm as a guide, a improved Multiple Instance Learning (MIL) method is proposed for human action recognition in video image sequences. Experiments show that the new method can quickly recognize human actions and achieve high recognition rates, and on the Weizmann database validate our analysis.

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20

Jia, Hai Long, and Kun Cao. "Mixed Features Based Improved Human Action Recognition Algorithm." Advanced Materials Research 989-994 (July 2014): 2731–34. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2731.

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The choice of the motion features affects the result of the human action recognition method directly. Many factors often influence the single feature differently, such as appearance of human body, environment and video camera. So the accuracy of action recognition is limited. On the basis of studying the representation and recognition of human actions, and giving full consideration to the advantages and disadvantages of different features, this paper proposes a mixed feature which combines global silhouette feature and local optical flow feature. This combined representation is used for human

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21

Zhang, Jing, Hong Lin, Weizhi Nie, Lekha Chaisorn, Yongkang Wong, and Mohan S. Kankanhalli. "Human Action Recognition Bases on Local Action Attributes." Journal of Electrical Engineering and Technology 10, no. 3 (2015): 1264–74. http://dx.doi.org/10.5370/jeet.2015.10.3.1264.

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22

Rai, Ankush, and Jagadeesh Kannan R. "A REVIEW ON MACHINE LEARNING ALGORITHMS ON HUMAN ACTION RECOGNITION." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (2017): 406. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19977.

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Human action recognition is a vital field of computer vision research. Its applications incorporate observation frameworks, patient monitoring frameworks, and an assortment of frameworks that include interactions between persons and electronic gadgets, for example, human-computer interfaces. The vast majority of these applications require an automated recognition of abnormal or anomalistic action states, made out of various straightforward (or nuclear) actions of persons. This study gives an overview of different best in class research papers on human movement recognition. Open datasets intend

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23

Vishwakarma, Prof Abhishek, Prof Pankaj Pali, Roshan Sen, and Naman Chourasiya. "Human Action Recognition using Methods Deep Learning." International Journal of Innovative Research in Science,Engineering and Technology 12, no. 06 (2023): 9024–27. http://dx.doi.org/10.15680/ijirset.2023.1206158.

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The objective of human action recognition is to recognize and comprehend human behaviour in videos with the help of expertly matched tags. Thus, there are a plethora of applications for human action recognition, such as video surveillance and patient monitoring. In this paper, three methods are developed and implemented to address these challenging tasks. Convolutional neural networks (CNNs) are the basis for the CNN+LSTM, 3D CNN, and TwoStream CNN algorithms, which are used to recognize human actions in videos.

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24

KEÇELI, ALI SEYDI, and AHMET BURAK CAN. "RECOGNITION OF BASIC HUMAN ACTIONS USING DEPTH INFORMATION." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 02 (2014): 1450004. http://dx.doi.org/10.1142/s0218001414500049.

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Human action recognition using depth sensors is an emerging technology especially in game console industry. Depth information can provide robust features about 3D environments and increase accuracy of action recognition in short ranges. This paper presents an approach to recognize basic human actions using depth information obtained from the Kinect sensor. To recognize actions, features extracted from angle and displacement information of joints are used. Actions are classified using support vector machines and random forest (RF) algorithm. The model is tested on HUN-3D, MSRC-12, and MSR Actio

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25

Othman, Nashwan Adnan, and Ilhan Aydin. "Challenges and Limitations in Human Action Recognition on Unmanned Aerial Vehicles: A Comprehensive Survey." Traitement du Signal 38, no. 5 (2021): 1403–11. http://dx.doi.org/10.18280/ts.380515.

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An Unmanned Aerial Vehicle (UAV), commonly called a drone, is an aircraft without a human pilot aboard. Making UAVs that can accurately discover individuals on the ground is very important for various applications, such as people searches, and surveillance. UAV integration in smart cities is challenging, however, because of problems and concerns such as privacy, safety, and ethical/legal use. Human action recognition-based UAVs can utilize modern technologies. Thus, it is essential for future development of the aforementioned applications. UAV-based human activity recognition is the procedure

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Morshed, Md Golam, Tangina Sultana, Aftab Alam, and Young-Koo Lee. "Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities." Sensors 23, no. 4 (2023): 2182. http://dx.doi.org/10.3390/s23042182.

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Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature learning-based representations for action recognition has emerged in recent years, due to the widespread use of deep learning-based features. This study presents an in-depth analysis of human activity recognition that investigates recent developments in computer vision. Augmented reality, human–computer interact

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27

Degardin, Bruno, and Hugo Proença. "Human Behavior Analysis: A Survey on Action Recognition." Applied Sciences 11, no. 18 (2021): 8324. http://dx.doi.org/10.3390/app11188324.

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The visual recognition and understanding of human actions remain an active research domain of computer vision, being the scope of various research works over the last two decades. The problem is challenging due to its many interpersonal variations in appearance and motion dynamics between humans, without forgetting the environmental heterogeneity between different video images. This complexity splits the problem into two major categories: action classification, recognising the action being performed in the scene, and spatiotemporal action localisation, concerning recognising multiple localised

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Jahagirdar, Aditi, and Rashmi Phalnikar. "Comparison of feed forward and cascade forward neural networks for human action recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 892. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp892-899.

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Humans can perform an enormous number of actions like running, walking, pushing, and punching, and can perform them in multiple ways. Hence recognizing a human action from a video is a challenging task. In a supervised learning environment, actions are first represented using robust features and then a classifier is trained for classification. The selection of a classifier does affect the performance of human action recognition. This work focuses on the comparison of two structures of the neural network, namely, feed forward neural network and cascade forward neural network, for human action r

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Jahagirdar, Aditi, and Rashmi Phalnikar. "Comparison of feed forward and cascade forward neural networks for human action recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 892–99. https://doi.org/10.11591/ijeecs.v25.i2.pp892-899.

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Humans can perform an enormous number of actions like running, walking, pushing, and punching, and can perform them in multiple ways. Hence recognizing a human action from a video is a challenging task. In a supervised learning environment, actions are first represented using robust features and then a classifier is trained for classification. The selection of a classifier does affect the performance of human action recognition. This work focuses on the comparison of two structures of the neural network, namely, feed forward neural network and cascade forward neural network, for human action r

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30

Perera, Asanka G., Yee Wei Law, and Javaan Chahl. "Drone-Action: An Outdoor Recorded Drone Video Dataset for Action Recognition." Drones 3, no. 4 (2019): 82. http://dx.doi.org/10.3390/drones3040082.

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Aerial human action recognition is an emerging topic in drone applications. Commercial drone platforms capable of detecting basic human actions such as hand gestures have been developed. However, a limited number of aerial video datasets are available to support increased research into aerial human action analysis. Most of the datasets are confined to indoor scenes or object tracking and many outdoor datasets do not have sufficient human body details to apply state-of-the-art machine learning techniques. To fill this gap and enable research in wider application areas, we present an action reco

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31

Volvoikar, Neema V. "A Survey on Human Action Recognition." International Journal for Research in Applied Science and Engineering Technology 7, no. 5 (2019): 1842–45. http://dx.doi.org/10.22214/ijraset.2019.5308.

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32

Liu, Guocheng, Caixia Zhang, Qingyang Xu, et al. "I3D-Shufflenet Based Human Action Recognition." Algorithms 13, no. 11 (2020): 301. http://dx.doi.org/10.3390/a13110301.

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In view of difficulty in application of optical flow based human action recognition due to large amount of calculation, a human action recognition algorithm I3D-shufflenet model is proposed combining the advantages of I3D neural network and lightweight model shufflenet. The 5 × 5 convolution kernel of I3D is replaced by a double 3 × 3 convolution kernels, which reduces the amount of calculations. The shuffle layer is adopted to achieve feature exchange. The recognition and classification of human action is performed based on trained I3D-shufflenet model. The experimental results show that the

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33

An, Jiahui, Xinrong Cheng, Qing Wang, Hong Chen, Jiayue Li, and Shiji Li. "Human action recognition based on Kinect." Journal of Physics: Conference Series 1693 (December 2020): 012190. http://dx.doi.org/10.1088/1742-6596/1693/1/012190.

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Zhong Zhang, Chunheng Wang, Baihua Xiao, Wen Zhou, and Shuang Liu. "Attribute Regularization Based Human Action Recognition." IEEE Transactions on Information Forensics and Security 8, no. 10 (2013): 1600–1609. http://dx.doi.org/10.1109/tifs.2013.2258152.

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35

Zhou, Wen, Chunheng Wang, Baihua Xiao, and Zhong Zhang. "Human action recognition using weighted pooling." IET Computer Vision 8, no. 6 (2014): 579–87. http://dx.doi.org/10.1049/iet-cvi.2013.0306.

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Masoud, Osama, and Nikos Papanikolopoulos. "A method for human action recognition." Image and Vision Computing 21, no. 8 (2003): 729–43. http://dx.doi.org/10.1016/s0262-8856(03)00068-4.

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37

Ahad, Md Atiqur Rahman. "Smart Approaches for Human Action Recognition." Pattern Recognition Letters 34, no. 15 (2013): 1769–70. http://dx.doi.org/10.1016/j.patrec.2013.07.006.

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38

Yi, Yang, and Yikun Lin. "Human action recognition with salient trajectories." Signal Processing 93, no. 11 (2013): 2932–41. http://dx.doi.org/10.1016/j.sigpro.2013.05.002.

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Iglesias-Ham, Mabel, Edel Bartolo García-Reyes, Walter George Kropatsch, and Nicole Maria Artner. "Convex Deficiencies for Human Action Recognition." Journal of Intelligent & Robotic Systems 64, no. 3-4 (2011): 353–64. http://dx.doi.org/10.1007/s10846-011-9540-1.

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Parameswaran, Vasu, and Rama Chellappa. "Human action-recognition using mutual invariants." Computer Vision and Image Understanding 98, no. 2 (2005): 294–324. http://dx.doi.org/10.1016/j.cviu.2004.09.002.

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Chen, Huafeng, Jun Chen, and Ruimin Hu. "Structural iMoSIFT for human action recognition." Wuhan University Journal of Natural Sciences 21, no. 3 (2016): 262–66. http://dx.doi.org/10.1007/s11859-016-1169-2.

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Parameswaran, Vasu, and Rama Chellappa. "View Invariance for Human Action Recognition." International Journal of Computer Vision 66, no. 1 (2006): 83–101. http://dx.doi.org/10.1007/s11263-005-3671-4.

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Asteriadis, Stylianos, and Petros Daras. "Landmark-based multimodal human action recognition." Multimedia Tools and Applications 76, no. 3 (2016): 4505–21. http://dx.doi.org/10.1007/s11042-016-3945-6.

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Liu, Chang, and Pong C. Yuen. "Human action recognition using boosted EigenActions." Image and Vision Computing 28, no. 5 (2010): 825–35. http://dx.doi.org/10.1016/j.imavis.2009.07.009.

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Gao, Zan, Hua Zhang, Anan A. Liu, Guangping Xu, and Yanbing Xue. "Human action recognition on depth dataset." Neural Computing and Applications 27, no. 7 (2015): 2047–54. http://dx.doi.org/10.1007/s00521-015-2002-0.

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Dileep, Labana, and Modi Kirit. "Human Action Recognition Using Dense Trajectories." Indian Journal of Science and Technology 16, no. 43 (2024): 3846–53. https://doi.org/10.17485/IJST/v16i43.2408.

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Abstract <strong>Objective:</strong> To develop a robust and effective computer vision system that can automatically identify and classify human actions in video data, considering the temporal dynamics and various environmental conditions. This technology has numerous applications in surveillance, human-computer interaction, and video analysis. <strong>Methods:</strong> The key methods for dense trajectory extraction include the dense optical flow, which computes motion vectors for each point, and the use of key point detectors like the Scale-Invariant Feature Transform (SIFT) o

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47

Dong, Kangnan, and Wei Qi Yan. "Player Performance Analysis in Table Tennis Through Human Action Recognition." Computers 13, no. 12 (2024): 332. https://doi.org/10.3390/computers13120332.

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This paper aims to enhance the effectiveness of table tennis coaching and player performance analysis through human action recognition by using deep learning. In the field of video analysis, human action recognition has emerged as a highly researched area. Beyond post-session analysis, it has the potential for real-time applications, such as providing instant feedback or comparing ideal motions with actual player movements. However, the complexity of human actions presents significant challenges. To address these issues, in this paper, we combine the latest computer vision and deep learning al

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Diraco, Giovanni, Gabriele Rescio, Pietro Siciliano, and Alessandro Leone. "Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing." Sensors 23, no. 11 (2023): 5281. http://dx.doi.org/10.3390/s23115281.

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Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies in homes and cities to enhance the quality of life for citizens. Sensing and human action recognition are crucial aspects of this concept. Smart living applications span various domains, such as energy consumption, healthcare, transportation, and education, which greatly benefit from effective human action recognition. This field, originating from computer vision, seeks to recognize human actions and activities using not only visual data but also many other sensor mod

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Ramya, P., and R. Rajeswari. "A Silhouette based Human Action Recognition Technique Using Deep Stacked Auto Encoder." Journal of Advanced Research in Dynamical and Control Systems 12, no. 1 (2020): 104–12. http://dx.doi.org/10.5373/jardcs/v12i1/20201016.

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Al-Akam, Rawya, and Dietrich Paulus. "Local Feature Extraction from RGB and Depth Videos for Human Action Recognition." International Journal of Machine Learning and Computing 8, no. 3 (2018): 274–79. http://dx.doi.org/10.18178/ijmlc.2018.8.3.699.

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