RGBD-HuDaAct: A color-depth video database for human daily activity recognition

By Bingbing Ni, G. Wang, Pierre Moulin. 2011

401 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis (2016, background): “RGBD-HuDaAct [23] was one of the largest datasets.”
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (2019, background): “The RGBD-HuDaAct dataset [13] was one of the largest datasets.”
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos (2016, methodology): “RGBD-HuDaAct [39] is a large size benchmarks for human daily action recognition in RGB+D.”
RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “The curves under the images are the accelerometer data at 50 Hz of devices attached to the knife, the mixing spoon, the small spoon, the peeler, the glass, the oil bottle, and the pepper dispenser mentioned datasets above, another 26 extra RGB-D datasets for different applications are also added int”
RGB-D-based action recognition datasets: A survey (2016, neutral): “RGBD-HuDaAct [11]( http://adsc.illinois.edu/sites/default/files/files/ADSC-_RGBD-_dataset-_- download-_instructions.pdf ) was collected by Advanced Digital Sciences Center Singapore in 2011.”
Learning human activities and object affordances from RGB-D videos (2012, background): “Recently, with the availability of inexpensive RGB-D sensors, some works (Li et al., 2010; Ni et al., 2011; Sung et al., 2011; Zhang and Parker, 2011; Sung et al., 2012) consider detecting human activities from RGB-D videos.”
PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding (2017, neutral): “MSR-Action3D [17] 20 567 567 1 D+Skeleton No 2010 RGBD-HuDaAct [21] 13 1189 1189 1 RGB+D No 2011 MSR-DailyActivity [43] 16 320 320 1 RGB+D+Skeleton No 2012 Act4 [8] 14 6844 6844 1 RGB+D No 2012 UTKinect-Action [53] 10 200 200 1 RGB+D+Skeleton No 2012 3D Action Pairs [23] 12 360 360 1 RGB+D+Skeleton ”
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes (2017, background): “A large number of RGB-D datasets have been captured and made publicly available for training and benchmarking [56, 34, 50, 65, 79, 83, 74, 4, 58, 81, 15, 55, 1, 68, 30, 51, 21, 48, 43, 92, 80, 61, 72, 93, 36, 16, 35, 57, 40, 29, 70, 52, 45, 95, 75, 9, 33, 85, 71, 32, 3, 10, 78, 2].”
Anticipating Human Activities Using Object Affordances for Reactive Robotic Response (2013, methodology): “We will do so by sampling several possible graph structures by augmenting the graph in time, each of which we will call an anticipatory temporal conditional random field (ATCRF).”
Structure-Preserving Binary Representations for RGB-D Action Recognition (2016, background): “Similarly, Ni et al. [38] designed two color-depth fusion schemes for human activity recognition.”
Graph Distillation for Action Detection with Privileged Modalities (2017, background): “Specifically, with the availability of depth sensors and joint tracking algorithms, extensive research has been done on action classification and detection in RGB-D videos [39,47,48,61] as well as skeleton sequences [24,30,31,32,46,63].”
An efficient part-based approach to action recognition from RGB-D video with BoW-pyramid representation (2013, methodology): “, KTH [5], RGBD-HuDaAct [6], and OccData recorded by ourselves.”
Accurate 3D action recognition using learning on the Grassmann manifold (2015, background): “Bingbing et al.[29] use depth maps to extract STIP and encode Motion His-176 tory Image (MHI) in a framework combining color and depth information.”
Smartphone Sensors for Health Monitoring and Diagnosis (2019, background): “Most existing activity monitoring systems rely on a network of cameras fixed at key locations in a home [22,129].”
3D SMoSIFT: three-dimensional sparse motion scale invariant feature transform for activity recognition from RGB-D videos (2014, background): “Ni et al.(8) proposed a depth-layered multichannel STIPs framework, which divides STIPs into several depth-layered channels and then STIPs within different channels are pooled independently.”
Integrating multi-stage depth-induced contextual information for human action recognition and localization (2013, background): “[12] developed two color-depth fusion schemes for feature representation from the most representative feature representation methods in human action recognition.”