Activity recognition with volume motion templates and histograms of 3D gradients

Doğan, Emre; Eren, Gönen; Wolf, Christian; Başkurt, Atilla

doi:10.1109/icip.2015.7351642

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…Treating each depth point as a pixel, they then computed the dense trajectory for their features. In a different approach, Dogan et al [61] stacked these depth maps, computed volume motion templates (VMTs – similar to MHI) and rotated these VMTs with respect to the canonical representation to achieve view invariance. They then extracted 3D oriented gradient as features for their proposed system.…”

Section: Updated Reviewmentioning

confidence: 99%

“…For depth‐based features, a popular approach is to apply HOG on DMMs and concatenate these HOGs for all the different views [23, 82]. In order to increase the robustness against variation is action speed, either concatenation with accumulated temporal energy [13, 61, 75, 82] or fusion with energy obtained from multi‐scale temporal pyramid [60, 76] has been adopted.…”

Section: Updated Reviewmentioning

confidence: 99%

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Advances in human action recognition: an updated survey

Abu-Bakar

2019

IET image process

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Research in human activity recognition (HAR) has seen tremendous growth and continuously receiving attention from both the Computer Vision and the Image Processing communities. Due to the existence of numerous publications in this field, undoubtedly, there have been a number of review papers on this subject that categorise these techniques. Many of the recent works have started to tackle more challenging problems and these proposed techniques are addressing more realistic real‐world scenarios. Conspicuously, an updated survey that covers these methods is timely due. To simplify the categorisation, this study takes a two‐layer hierarchical approach. At the top level, the categorisation is based on the basic process flow of HAR, i.e. input data‐type, features‐type, descriptor‐type, and classifier‐type. At the second layer, each of these components is further subcategorised based on the diversity of the proposed methods. Finally, a remark on the coming popularity of deep learning approach in this field is also given.

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Section: Updated Reviewmentioning

confidence: 99%