Continuous human action recognition in real time

Guo, Ping; Miao, Zhenjiang; Shen, Yuan; Xu, Wanru; Zhang, Dianyong

doi:10.1007/s11042-012-1084-2

Cited by 12 publications

(6 citation statements)

References 30 publications

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“…Different from the published CHAR methods [ 18 , 19 , 20 ], the proposed algorithm does not detect the start and end points of each human action. We divide feature sequences into pose feature segments and motion feature segments, and thus poses and movements of each human action are embedded in the feature segments.…”

Section: Resultsmentioning

confidence: 97%

“…However, it is a tricky problem to decide the sliding window size for such methods. A generative model based on the bag-of-words representation and the translation and scale invariant probabilistic Latent Semantic Analysis model (TSI-pLSA) is proposed in [ 18 ], the start and end frames of one human action are detected according the posterior probability using a threshold-based method. In [ 19 ], the authors use Hidden Markov Model (HMM) based action modeling method to model various human actions, and employ action spotter method to filter meaningless human actions and to detect the start and end points of human actions.…”

Section: Related Workmentioning

confidence: 99%

“…Compared with the CHAR methods [ 18 , 19 ] which need to detect the start and end points of human actions beforehand, the proposed algorithm does not detect the start and end points of human actions explicitly. The proposed algorithm divides feature sequences into pose feature segments and motion feature segments, and the potential-difference-based segmentation method ensures that almost all the boundaries of human actions can be conformed with boundaries of the first and last segments within each human action’s feature sequence.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Online Continuous Human Action Recognition Algorithm Based on the Kinect Sensor

Zhu

Zhang

Shen

et al. 2016

Sensors

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Continuous human action recognition (CHAR) is more practical in human-robot interactions. In this paper, an online CHAR algorithm is proposed based on skeletal data extracted from RGB-D images captured by Kinect sensors. Each human action is modeled by a sequence of key poses and atomic motions in a particular order. In order to extract key poses and atomic motions, feature sequences are divided into pose feature segments and motion feature segments, by use of the online segmentation method based on potential differences of features. Likelihood probabilities that each feature segment can be labeled as the extracted key poses or atomic motions, are computed in the online model matching process. An online classification method with variable-length maximal entropy Markov model (MEMM) is performed based on the likelihood probabilities, for recognizing continuous human actions. The variable-length MEMM method ensures the effectiveness and efficiency of the proposed CHAR method. Compared with the published CHAR methods, the proposed algorithm does not need to detect the start and end points of each human action in advance. The experimental results on public datasets show that the proposed algorithm is effective and highly-efficient for recognizing continuous human actions.

show abstract

Section: Resultsmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Online Continuous Human Action Recognition Algorithm Based on the Kinect Sensor

Zhu

Zhang

Shen

et al. 2016

Sensors

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show abstract

“…In [10], frame analysis is employed and 81.0% accuracy is reported on the IXMAS dataset. In the case of the Weizmann dataset, for example in [9], CHAR is performed and a score of 97.8% is reached. Segment analysis is employed in this case, although the rate of correctly classified segments is computed based on a 60% overlap with the ground truth.…”

Section: Resultsmentioning

confidence: 99%

“…The first option performed better, since it does not classify specific temporal moments, but aligns a globally optimal segmentation taking into account movement direction. In [9], start and end key frames of actions are identified. Segmentation is performed based on the posterior probability of model matching considering recognition rounds.…”

Section: Related Workmentioning

confidence: 99%

Continuous Human Action Recognition in Ambient Assisted Living Scenarios

Chaaraoui

Flórez-Revuelta²

2015

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Ambient assisted living technologies and services make it possible to help elderly and impaired people and increase their personal autonomy. Specifically, vision-based approaches enable the recognition of human behaviour, which in turn allows to build valuable services upon. However, a main constraint is that these have to be able to work online and in real time. In this work, a human action recognition method based on a bag-of-key-poses model and sequence alignment is extended to support continuous human action recognition. The detection of action zones is proposed to locate the most discriminative segments of an action. For the recognition, a method based on a sliding and growing window approach is presented. Furthermore, an evaluation scheme particularly designed for ambient assisted living scenarios is introduced. Experimental results on two publicly available datasets are provided. These show that the proposed action zones lead to a significant improvement and allow real-time processing.

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Recognition of Human Continuous Action with 3D CNN

2017

Lecture Notes in Computer Science

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Continuous human action recognition in real time

Cited by 12 publications

References 30 publications

An Online Continuous Human Action Recognition Algorithm Based on the Kinect Sensor

An Online Continuous Human Action Recognition Algorithm Based on the Kinect Sensor

Continuous Human Action Recognition in Ambient Assisted Living Scenarios

Recognition of Human Continuous Action with 3D CNN

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