Berkeley MHAD: A comprehensive Multimodal Human Action Database

Ofli, Ferda; Chaudhry, Rizwan; Kurillo, Gregorij; Vidal, René; Bajcsy, Růžena

doi:10.1109/wacv.2013.6474999

Cited by 401 publications

(275 citation statements)

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“…Human Skeletal Configurations Figure 2(a) provides an example of the human skeleton structure from the Berkeley MHAD [20]. Following [11], we divide the human skeleton into several topological parts such as chains, single X/Y junctions, and double X/Y junctions.…”

Section: Hierarchical Medial-axis Template Models Formentioning

confidence: 99%

Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action Recognition

Chaudhry

Ofli

Kurillo

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops

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162

101

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Over the last few years, with the immense popularity of the Kinect, there has been renewed interest in developing methods for human gesture and action recognition from 3D data. A number of approaches have been proposed that extract representative features from 3D depth data, a reconstructed 3D surface mesh or more commonly from the recovered estimate of the human skeleton. Recent advances in neuroscience have discovered a neural encoding of static 3D shapes in primate infero-temporal cortex that can be represented as a hierarchy of medial axis and surface features. We hypothesize a similar neural encoding might also exist for 3D shapes in motion and propose a hierarchy of dynamic medial axis structures at several spatio-temporal scales that can be modeled using a set of Linear Dynamical Systems (LDSs). We then propose novel discriminative metrics for comparing these sets of LDSs for the task of human activity recognition. Combined with simple classification frameworks, our proposed features and corresponding hierarchical dynamical models provide the highest human activity recognition rates as compared to state-of-the-art methods on several skeletal datasets.

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Section: Hierarchical Medial-axis Template Models Formentioning

confidence: 99%

Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action Recognition

Chaudhry

Ofli

Kurillo

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops

Self Cite

162

101

View full text Add to dashboard Cite

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“…Raw inertial sensor data are used extensively, due to their ability to capture instantaneous features of local character and, thus, lead to a rich source of information for action classification. Statistical [23], expressivity [5] and frequency domain parameters [17], on the other hand, although local, convey a summary of an action for different parts of the human body and, thus, they can be time independent. Such parameters usually depend on efficient tracking in video sequences, which is a challenging area of research on its own, attracting the attention of numerous researchers.…”

Section: Related Workmentioning

confidence: 99%

“…Experimental results are also presented on the recently published dataset, Berkely MHAD (Multimodal Human Action Database), described in [23]. The dataset comprises 11 actions performed by 12 subjects, with each subject performing a set of 5 repetitions of each action.…”

Section: Berkeley Mhad Databasementioning

confidence: 99%

“…All accelerometer data were employed (6 three-dimensional vectors), and their variances in 15 temporal windows was considered, leading to a total of 270 features per action. Similar to [23], the 7 first subjects were used for training, while the last 5 were used for testing.…”

Section: Berkeley Mhad Databasementioning

confidence: 99%

“…Table 2 compares the results achieved using the proposed method and the method used in [23], where multiclass Multiple Kernel Learning was used, while, Figs. 5, 6 and 7 are indicative of the discriminative power of the proposed technique.…”

Section: Berkeley Mhad Databasementioning

confidence: 99%

See 2 more Smart Citations

Landmark-based multimodal human action recognition

Asteriadis

Daras

2016

Multimed Tools Appl

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Human activity recognition has received a lot of attention recently, mainly thanks to the advancements in sensing technologies and systems' increasing computational power. However, complexity in human movements, sensing devices' noise and person-specific characteristics impose challenges that still remain to be overcome. In the proposed work, a novel, multi-modal human action recognition method is presented for handling the aforementioned issues. Each action is represented by a basis vector and spectral analysis is performed on an affinity matrix of new action feature vectors. Using modality-dependent kernel regressors for computing the affinity matrix, complexity is reduced and robust lowdimensional representations are achieved. The proposed scheme supports online adaptivity of modalities, in a dynamic fashion, according to their automatically inferred reliability. Evaluation on three publicly available datasets demonstrates the potential of the approach.

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