Human activities segmentation and location of key frames based on 3D skeleton

Wang, Yongxiong; Shi, Yubo

doi:10.1109/chicc.2014.6895749

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Azouji and Azimifar [31] considered the representative frames that include a set of salient images extracted from the underlying video as key frames. Wang and Shi [32] located the key frames by the sphere of maximum energy information. Guan et al [33] proposed a key point based on a framework to address the key frame selection problem, so that local features can be employed to select key frames.…”

Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

Human interaction recognition fusing multiple features of depth sequences

Mao

Chen

et al. 2017

IET Computer Vision

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Human interaction recognition has played a major role in building intelligent video surveillance systems. Recently, depth data captured by the emerging RGB-D sensors began to show its importability in human interaction recognition. This study proposes a novel framework for human interaction recognition using depth information including an algorithm to reconstruct depth sequence with as few key frames as possible. The proposed framework includes two essential modules. First, key frames extraction by sparse constraint, then the fusion multi-feature, is constructed by using two types of available features and Max-pooling, respectively. Finally, multiple features are directly sent to the SVM for the recognition of the human activity. This study explores the static and dynamic feature fusion method to improve the recognition performance with contextual relevance of continuous frames. A weight is used to fuse shape and optical flow features, which not only enhance the description capability of human behavioural characteristics in the spatiotemporal domain, but also effectively reduces the adverse impact of certain distortion point of interest for target recognition. Experimental results show that the proposed approach yields considerable performance improvement over the state-of-the-art approaches with respect to accuracy on a public action dataset.

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Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

Human interaction recognition fusing multiple features of depth sequences

Mao

Chen

et al. 2017

IET Computer Vision

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“…As a shape descriptor, the skeleton has the characteristics of translation, rotation, scaling, and perspective invariance, which can effectively reflect the connectivity and topology of the original object shape 1 . At present, image skeleton extraction algorithms are widely used in target recognition, shape matching and various detection applications [2][3][4][5][6][7][8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

Research on skeleton extraction algorithm based on distance transform

Yang¹,

Yang²,

Lan³

et al. 2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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Aiming at the problem that the position of skeleton obtained by the method based on topology refinement is not accurate enough, and the connectivity of the skeleton obtained by distance transform cannot be guaranteed, this paper combines the advantages of the topology refinement algorithm and distance transformation algorithm to propose an improved skeleton extraction algorithm. This method first obtains the skeleton points through distance transform, and then obtains the initial skeleton through the refinement method; last uses the skeleton points obtained by distance transformation to screen each branch obtained through the refinement method to ensure the accuracy of the skeleton in the later applications. Experiments on the MPEG7 dataset demonstrate the effectiveness of the algorithm.

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