Range-Sample Depth Feature for Action Recognition

By Cewu Lu, Jiaya Jia, Chi-Keung Tang. 2014

114 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis (2016, methodology): “Recently reported results on this dataset also achieved very high accuracies [20, 15, 22, 31].”
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (2019, background): “Some reported results on this dataset also achieved very high accuracies [40], [41], [42], [43].”
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos (2016, background): “Recent single modality action recognition methods on depth signals can be divided into two major groups: depth map analysis methods [35, 41, 45, 79] and skeleton based methods [36, 65, 11, 73, 74].”
Histogram of Oriented Principal Components for Cross-View Action Recognition (2014, background): “HOPC is extracted at a point by projecting all three eigenvectors of the pointcloud within a local spatio-temporal support volume, scaled by their corresponding eigenvalues, on the vertices of a regular dodecahedron.”
Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks (2017, methodology): “These methods either extracted features from one modality: depth [49, 60, 29, 59, 24, 51, 52] or RGB [27, 33] or skeleton [43, 55, 56, 4, 34, 19], or fuse the features extracted separately from them at a later stage [28, 16, 10, 58].”
Unsupervised Learning of Long-Term Motion Dynamics for Videos (2017, methodology): “State-of-the-art methods [15, 16] for RGB-D/depth-based activity recognition report human level performance on well-established datasets like MSRDailyActivity3D [14] and CAD-120 [33].”
Spatially and Temporally Structured Global to Local Aggregation of Dynamic Depth Information for Action Recognition (2018, methodology): “In [13], a fast binary range-sample feature was proposed based on a test statistic by carefully designing the sampling scheme to exclude most pixels that fall into the background and to incorporate spatio-temporal cues.”
Structured Images for RGB-D Action Recognition (2017, unknown): “09% Range Sample [20] 95.”
Deep Bilinear Learning for RGB-D Action Recognition (2018, background): “The geometric information depicted in depth sequences can be used to characterize action [24, 36, 42, 26, 18].”
Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks (2016, methodology): “Since the ﬁrst work [24] reported in 2010, many methods [25]–[29] have been proposed using speciﬁcally hand-crafted feature descriptors extracted from depth.”
Large-scale Isolated Gesture Recognition using Convolutional Neural Networks (2016, methodology): “In [10], a fast binary range-sample feature was proposed based on a test statistic by carefully designing the sampling scheme to exclude most pixels that fall into the background and to incorporate spatio-temporal cues.”
Human Action Recognition and Prediction: A Survey (2018, background): “feature for depth data was proposed in [160].”
Action Recognition From Depth Maps Using Deep Convolutional Neural Networks (2016, methodology): “However, the Range Sample [20] method has a mechanism to remove/reduce the interference from the background using skeleton data for preprocessing.”
Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks (2018, methodology): “In [38], a fast binary range-sample feature was proposed based on a test statistic by carefully de-”
Learning features combination for human action recognition from skeleton sequences (2017, methodology): “Some methods use solely depth maps for action recognition [9, 11, 14, 16, 28].”
Multitype Activity Recognition in Robot-Centric Scenarios (2015, background): “A new segment starts if a new person enters the scene, if someone leaves the scene, or if someone starts performing a different activity.”