Recognition of human actions using texture descriptors

Kellokumpu, Vili; Zhao, Guoying; Pietikäinen, Matti

doi:10.1007/s00138-009-0233-8

Cited by 84 publications

(38 citation statements)

References 18 publications

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“…After that, the HOG features are supplied to a support vector machine (SVM) for action classification. In the other work [KZP11] a histogram of the local binary pattern (LBP) was extracted from MHI and MEI as temporal templates to represent human action and the Hidden Markov Models (HMMs) is used to represent and recognize a temporal behavior of the action. The human action recognition can also be described by using the dynamic texture feature descriptors on spatio-temporal domains [CKZP08] and this features are used for human detection to extract LBP-TOP features in spatiotemporal domains from image data, these features are used to detect human bounding volumes and to describe human movements.…”

Section: Literature Reviewmentioning

confidence: 99%

“…All these application domains have own demands, but generally, algorithms have the ability for detecting and recognizing several actions in real time. The designed algorithm should be able to handle different forms of environment and all variations in performing actions because of the different appearance and movement of people [KZP11]. In this paper, we adopt the ideas of spatio-temporal analysis and global features extraction.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Human Action Recognition from RGBD Videos based on Retina Model and Local Binary Pattern Features

Al-Akam¹,

Al-Darraji²,

Paulus³

2018

CSRN

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Human action recognition from the videos is one of the most attractive topics in computer vision during the last decades due to wide applications development. This research has mainly focused on learning and recognizing actions from RGB and Depth videos (RGBD). RGBD is a powerful source of data providing the aligned depth information which has great ability to improve the performance of different problems in image understanding and video processing. In this work, a novel system for human action recognition is proposed to extract distinctive spatio and temporal feature vectors for presenting the spatio-temporal evolutions from a set of training and testing video sequences of different actions. The feature vectors are computed in two steps: The First step is the motion detection from all video frames by using spatio-temporal retina model. This model gives a good structuring of video data by removing the noise and illumination variation and is used to detect potentially salient areas, these areas represent the motion information of the moving object in each frame of video sequences. In the Second step, because of human motion can be seen as a type of texture pattern, the local binary pattern descriptor (LBP) is used to extract features from the spatio-temporal salient areas and formulated them as a histogram to make the bag of feature vectors. To evaluate the performance of the proposed method, the k-means clustering, and Random Forest classification is applied on the bag of feature vectors. This approach is demonstrated that our system achieves superior performance in comparison with the state-of-the-art and all experimental results are depending on two public RGBD datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Action Recognition from RGBD Videos based on Retina Model and Local Binary Pattern Features

Al-Akam¹,

Al-Darraji²,

Paulus³

2018

CSRN

View full text Add to dashboard Cite

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“…Vision-based action recognition is a well-studied problem, and many methods [1,[5][6][7][8][17][18][19] have been proposed in recent years. There are a number of recent survey papers that offer a good overview of related works from the broad, generic scope [20][21][22] and selected perspectives [23,24].…”

Section: Related Workmentioning

confidence: 99%

“…Kellokumpu et al [19] used local binary patterns (LBP) to describe motion history and motion energy images which encodes shape and motion information respectively. Ahsan et al [13] use LBP features to describe mixed block-based directional MHI (DMHI) templates [31].…”

Section: Textural Featuresmentioning

confidence: 99%

Exploiting textures for better action recognition in low-quality videos

Rahman

See

2017

J Image Video Proc.

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Human action recognition is an increasingly matured field of study in the recent years, owing to its widespread use in various applications. A number of related research problems, such as feature representations, human pose and body parts detection, and scene/object context, are being actively studied. However, the general problem of video quality-a realistic issue in the face of low-cost surveillance infrastructure and mobile devices, has not been systematically investigated from various aspects. In this paper, we address the problem of action recognition in low-quality videos from a myriad of perspectives: spatial and temporal downsampling, video compression, and the presence of motion blurring and compression artifacts. To increase the resilience of feature representation in these type of videos, we propose to use textural features to complement classical shape and motion features. Extensive results were carried out on low-quality versions of three publicly available datasets: KTH, UCF-YouTube, HMDB. Experimental results and analysis suggest that leveraging textural features can significantly improve action recognition performance under low video quality conditions.

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“…A HMM is used to perceive human actions. In Kellokumpu et al's study [66], novel composition descriptors are proposed to portray motion and a HMM is utilized to show the temporal improvement of surface motion histograms. In Shi et al's study [67], a discriminative semi-Markov model methodology is proposed and with a specific end goal to effectively take care of the induction issue of at the same time portioning and perceiving distinctive actions they outlined a Viterbi like dynamic programming algorithm.…”

Section: State Model-based Approachesmentioning

confidence: 99%

A Review on Machine Learning Algorithms on Human Action Recognition

Rai

2017

Asian J Pharm Clin Res

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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 intended for the assessment of the recognition procedures are also discussed in this paper too, for comparing results of several methodologies on this datasets. We examine both the approaches produced for basic human actions and those for abnormal action states. These methodologies are taxonomically classified based on looking at the points of interest and constraints of every methodology. Space-time volume approaches and sequential methodologies that represent actions and perceive such action sets straightforwardly from images are discussed. Next, hierarchical recognition approaches for abnormal action states are introduced and looked at. Statistic-based methodologies, syntactic methodologies, and description-based methodologies for hierarchical recognition are examined in the paper.

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Recognition of human actions using texture descriptors

Cited by 84 publications

References 18 publications

Human Action Recognition from RGBD Videos based on Retina Model and Local Binary Pattern Features

Human Action Recognition from RGBD Videos based on Retina Model and Local Binary Pattern Features

Exploiting textures for better action recognition in low-quality videos

A Review on Machine Learning Algorithms on Human Action Recognition

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