People tracking in RGB-D data with on-line boosted target models

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RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “3 Three sampled color (left) and depth (right) images from RGB-D People Dataset [59]”
Tracking Revisited Using RGBD Camera: Unified Benchmark and Baselines (2013, methodology): “The publicly available RGBD People Dataset [22, 17] contains only one sequence with 1,132 frames captured with static cameras with only people moving, which is obviously not enough to evaluate tracking algorithms for general objects.”
SUN RGB-D: A RGB-D scene understanding benchmark suite (2015, unknown): “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].”
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 role of RGB-D benchmark datasets: an overview (2013, background): “They provide seven different scenes including RGB, depth and pose data in a txt-file.”
Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment (2018, background): “However, the insufficient depth information has resulted in bottlenecks in accuracy, efficiency, and timeliness of detection, recognition, tracking, and segmentation techniques based on traditional RGB images [11], [23],”