Human action recognition using Dynamic Time Warping

Sempena, Samsu; Maulidevi, Nur Ulfa; Aryan, Peb Ruswono

doi:10.1109/iceei.2011.6021605

Cited by 145 publications

(82 citation statements)

References 7 publications

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“…Veeraraghavan et al [14] suggest a modification of DTW algorithm to include the non-Euclidean space, in which the shape deformations take place, to match shape sequences for human movement. Moreover, Sempena et al [16] use DTW to recognize various human activities such as waving, punching and clapping. An exemplar-based sequential single-layered approach (as categorized in [95], where the approaches for human activity recognition are categorized into single-layered approaches and hierarchical approaches) for DTW is proposed in [16], and is used to attack speed variation.…”

Section: Dynamic Time Warping (Dtw)mentioning

confidence: 99%

“…Moreover, Sempena et al [16] use DTW to recognize various human activities such as waving, punching and clapping. An exemplar-based sequential single-layered approach (as categorized in [95], where the approaches for human activity recognition are categorized into single-layered approaches and hierarchical approaches) for DTW is proposed in [16], and is used to attack speed variation. The advantage of DTW is that it is fast and easy, but it might need extensive templates for various situations, resulting in high computation cost to match with these extensive templates.…”

Section: Dynamic Time Warping (Dtw)mentioning

confidence: 99%

“…Furthermore, in a healthcare system, the activity recognition can help the rehabilitation of patients, such as the automatic recognition of patient's action to facilitate the rehabilitation processes. There have been numerous research efforts reported for various applications based on human activity recognition, more specifically, home abnormal activity [1], ballet activity [2], tennis activity [3,4], soccer activity [5], human gestures [6], sport activity [7,8], human interaction [9], pedestrian traffic [10] and simple actions [11][12][13][14][15][16][17][18][19][20][21][22], and healthcare applications [1,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38]. In this paper, the video based technologies for human activity recognition will be extensively reviewed and discussed.…”

Section: Introductionmentioning

confidence: 99%

“…They can generally be categorized as dynamic time warping (DTW), generative models, discriminative models and others as shown in Figure 4. The dynamic time warping (DTW) [14,16], a method for measuring similarity between two temporal sequences, which may vary in time or speed, is one of the most common temporal classification algorithms due to its simplicity; however, DTW is not appropriate for a large number of classes with many variations. Some probability-based methods by generative models (dynamic classifiers) are proposed such as Hidden Markov Models (HMM) [1,4,6,17,[75][76][77] and Dynamic Bayesian Networks (DBN) [7,9,78].…”

Section: Introductionmentioning

confidence: 99%

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A Review on Video-Based Human Activity Recognition

et al. 2013

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This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms. In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition. Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems. Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications. Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems. OPEN ACCESSComputers 2013, 2 89

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Section: Dynamic Time Warping (Dtw)mentioning

confidence: 99%

Section: Dynamic Time Warping (Dtw)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Review on Video-Based Human Activity Recognition

et al. 2013

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“…One of the well-known classification algorithms is the Dynamic Time Warping (DTW) algorithm [30], which is a similarity measurement between two sequences by a dynamic programming approach. Sempena and others [31] use DTW to recognize human activities such as waving, punching, and clapping. DTW is simple and fast, but it might need extensive templates for various situations, which unfortunately results in high computational costs.…”

Section: Introductionmentioning

confidence: 99%

Human Action Recognition Based on 3D Human Modeling and Cyclic HMMs

Thuc

Hwang

et al. 2014

ETRI J

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Human action recognition is used in areas such as surveillance, entertainment, and healthcare. This paper proposes a system to recognize both single and continuous human actions from monocular video sequences, based on 3D human modeling and cyclic hidden Markov models (CHMMs). First, for each frame in a monocular video sequence, the 3D coordinates of joints belonging to a human object, through actions of multiple cycles, are extracted using 3D human modeling techniques. The 3D coordinates are then converted into a set of geometrical relational features (GRFs) for dimensionality reduction and discrimination increase. For further dimensionality reduction, k-means clustering is applied to the GRFs to generate clustered feature vectors. These vectors are used to train CHMMs separately for different types of actions, based on the Baum-Welch re-estimation algorithm. For recognition of continuous actions that are concatenated from several distinct types of actions, a designed graphical model is used to systematically concatenate different separately trained CHMMs. The experimental results show the effective performance of our proposed system in both single and continuous action recognition problems. Keywords I. IntroductionHuman action recognition is a growing topic in video analysis and understanding -one of the most popular areas in the community of computer vision -thanks to its applications in surveillance, entertainment, and healthcare. In surveillance, human action recognition can be used in conjunction with video camera footage to help with the recognition and analysis of human actions. In entertainment, human-computer interaction can be helped to appear more natural via human action recognition, which in turn can help increase the entertainment experience. In healthcare, human action recognition can help detect abnormal gaits or assist in a patient's rehabilitation through an analysis of their actions.However, it is challenging to recognize various human actions due to the high number of degrees of freedom associated with the average human body -namely, variations in human poses; variations in the colors of a person's clothing; changes in lighting and illumination; variations in viewpoints; and frequent self-occlusion. Moreover, the use of monocular video sequences further increases the difficulty for human action recognition.Generally, the two main stages in human action recognition are: the feature extraction and representation stage and the classification stage.In the feature extraction and representation stage, the features or characteristics of video frames, such as silhouette, shape, color, and motion, are extracted and represented in a systematic and efficient way. consists of stacking segmented silhouettes (frame by frame) to form a 3D spatial-temporal shape. In a similar way, Ke and others [2] build STVs, for shape-based matching, from image features that are based on the consecutive silhouettes of objects along a time axis, including spatial-temporal region extraction and region matching. Kim and oth...

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Bibliography

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Human Motion Capture and Identification for Assistive Systems Design in Rehabilitation

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3 Jointtype enumeration. https://msdn.microsoft.com/en-us/library/microsoft.kinect .jointtype.aspx. Accessed: 2015-05-22. 4 Kinect for windows features. https://www.microsoft.com/en-us/kinectforwindows/ meetkinect/features.aspx. Accessed: 2015-05-22. 5 Rokoko smartsuit pro. https://www.rokoko.com/en?gclid=CjwKCAjw_47YBRBxEiwAYu KdwynSSU8rlweQF-R8QBmqMjHq_aEzrUOffS7E0CYpc2pRCkeV9LAZ-xoCrT4QAvD_ BwE. Accessed: 2018-05-20. 6 Tracking users with kinect skeletal tracking. https://msdn.microsoft.com/en-us/library/ jj131025.aspx. Accessed: 2015-05-22.

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Human action recognition using Dynamic Time Warping

Cited by 145 publications

References 7 publications

A Review on Video-Based Human Activity Recognition

A Review on Video-Based Human Activity Recognition

Human Action Recognition Based on 3D Human Modeling and Cyclic HMMs

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