Pre-impact fall detection system using dynamic threshold and 3D bounding box

Nuth Otanasap; Poonpong Boonbrahm

doi:10.1117/12.2266822

8 February 2017 Pre-impact fall detection system using dynamic threshold and 3D bounding box

Nuth Otanasap, Poonpong Boonbrahm

Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 102250D (2017) https://doi.org/10.1117/12.2266822
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan

Abstract

Fall prevention and detection system have to subjugate many challenges in order to develop an efficient those system. Some of the difficult problems are obtrusion, occlusion and overlay in vision based system. Other associated issues are privacy, cost, noise, computation complexity and definition of threshold values. Estimating human motion using vision based usually involves with partial overlay, caused either by direction of view point between objects or body parts and camera, and these issues have to be taken into consideration. This paper proposes the use of dynamic threshold based and bounding box posture analysis method with multiple Kinect cameras setting for human posture analysis and fall detection. The proposed work only uses two Kinect cameras for acquiring distributed values and differentiating activities between normal and falls. If the peak value of head velocity is greater than the dynamic threshold value, bounding box posture analysis will be used to confirm fall occurrence. Furthermore, information captured by multiple Kinect placed in right angle will address the skeleton overlay problem due to single Kinect. This work contributes on the fusion of multiple Kinect based skeletons, based on dynamic threshold and bounding box posture analysis which is the only research work reported so far.

Citation Download Citation

Nuth Otanasap and Poonpong Boonbrahm "Pre-impact fall detection system using dynamic threshold and 3D bounding box", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102250D (8 February 2017); https://doi.org/10.1117/12.2266822

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