People detection in RGB-D data

By Luciano Spinello, Kai Oliver Arras. 2011

416 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

A survey of data fusion in smart city applications (2019, methodology): “Examples of the physical sensors include temperature [81], air quality [82], camera [83], ultrasonic [84], LiDAR [85], and etc. Note that, we categorize smart city application based on the data sources rather than the method they are acquired.”
RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “1 RGB-D people dataset RGB-D People dataset [59, 83] was founded in 2011 by social Robotics Lab (SRL) of University of Freiburg with the purpose of evaluating people detection and tracking algorithms for robotics, interactive systems and intelligent vehicles.”
Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “An alternate main methodology is starting with a ‘known’ prior, and fitting the silhouette or point cloud to this prior after the humans are localized [31], [82], [83].”
Kinect and RGBD Images: Challenges and Applications (2012, background): “Spinello and Arras [30] introduced a work to detect people in RGBD data.”
Pedestrian detection combining RGB and dense LIDAR data (2014, background): “Employing HOG on depth data is an idea that has also been explored in [25], where the so called Histogram of Oriented Depths (HOD) was proposed for human detection on Kinect data.”
A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2021, background): “[60] 2011 RGB-D People RGB + D Object recognition Contains more than 3000 RGB-D images”
Video-Based Human Behavior Understanding: A Survey (2013, methodology): “An adaptation of HOGs to RGB-D data is proposed by Spinello and Arras [68], where the direction of depth changes are locally encoded.”
Joint Human Detection and Head Pose Estimation via Multistream Networks for RGB-D Videos (2017, methodology): “[50], plane-view projection [36], two-stream CNN networks [42], fast R-CNN [33], and two other classic approaches: the depth-based detector proposed by the comboHOD detector [8] and the color-depth detector proposed by Choi et al.”
Local structure preserving discriminative projections for RGB-D sensor-based scene classification (2015, methodology): “[43] proposed ComboHOD to detect people in RGB-D data, while Zhang et al.”
Learning Discriminative Representations from RGB-D Video Data (2013, background): “In addition, some researchers [Spinello and Arras, 2011] combined multiple modalities such as dense depth data, optical flow maps, and color images to detect people in urban environments.”
RGBD Datasets: Past, Present and Future (2016, methodology): “The RGB-D people dataset [95, 70], the Kinect Tracking Precision dataset [80] and the RGBD Pedestrian Dataset [7] all track humans.”
The Seventh Visual Object Tracking VOT2019 Challenge Results (2019, background): “A number of datasets in RGB+depth (RGBD) tracking focus on pedestrian and hand tracking [23, 85, 14, 30] and object pose estimation for robotics [13, 80].”
People tracking in RGB-D data with on-line boosted target models (2011, background): “For more details, the reader is referred to [18].”
Structure-Preserving Binary Representations for RGB-D Action Recognition (2016, methodology): “Based on HOG, Spinello and Arras [4] proposed a method called Histogram of Oriented Depths (HOD) for depth description and probabilistically combined HOD and HOG into a Combo-HOD to detect people in urban environments.”
SUN RGB-D: A RGB-D scene understanding benchmark suite (2015, neutral): “There are many existing RGB-D datasets available [54] [47] [1] [25] [44] [60] [49] [45] [29] [66] [57] [52] [46] [16] [21] [73] [35] [67] [3] [41] [10] [63] [42] [64] [65] [48] [12] [33] [8] [50] [26] [6].”