Fall detection using ceiling-mounted 3D depth camera

By Michal Kepski, Bogdan Kwolek. 2015

98 citations recorded by the source corpus.

This paper proposes an algorithm for fall detection using a ceiling-mounted 3D depth camera. The lying pose is separated from common daily activities by a k-NN classifier, which was trained on features expressing head-floor distance, person area and shape's major length to width. In order to distinguish between intentional lying postures and accidental falls the algorithm also employs motion between static postures. The experimental validation of the algorithm was conducted on realistic depth image sequences of daily activities and simulated falls. It was evaluated on more than 45000 depth images and gave 0% error. To reduce the processing overload an accelerometer was used to indicate the potential impact of the person and to start an analysis of depth images.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

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].”
UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor (2015, background): “clude both depth and inertial sensor data are the Berkeley Multimodal Human Action Database (MHAD) [14] and the University of Rzeszow (UR) fall detection dataset [15].”
Elderly Fall Detection Systems: A Literature Survey (2020, methodology): “In the work by Kwolek and Kepski (2014, 2016), wearable devices and Kinect were connected to the Pandaboard through Bluetooth and cable, separately.”
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].”