Human Activity Recognition plays a crucial role in Ambient Assisted Living, where environmental sensors, including RGB-D cameras like Kinect, are increasingly used due to the many advantages they offer. However, challenges remain in handling occlusions, self-occlusions, and the limited field of view of the device, which can compromise tracking quality, especially when the subject is in motion or in non-optimal positions. In this work, we propose a novel method for combining data from two synchronized and calibrated Kinect devices to address occlusion and self-occlusion issues, improving tracking and expanding the tracking area. The fusion algorithm is based on the confidence level attributed to the joint's coordinates, the orientation and the distance of the subject from the devices. It was then tested on an acquisition where a subject performed a series of daily activities and simulated a dangerous situation such as falling to the floor. Experimental results demonstrate the effectiveness of fusion algorithm in reducing frame loss and improving the quality of the tracking process, particularly when the subject is between 0.5 and 3 meters from the devices and in a standing or sitting pose. However, when the subject is lying down or positioned beyond the optimal range, the method's effectiveness decreases.
Optimization and Fusion of Azure Kinect Data for Enhanced Skeleton Tracking
Russo L.;Sozzi S.;Soldi R.;Guerra B. M. V.;Ramat S.;Schmid M.
2025-01-01
Abstract
Human Activity Recognition plays a crucial role in Ambient Assisted Living, where environmental sensors, including RGB-D cameras like Kinect, are increasingly used due to the many advantages they offer. However, challenges remain in handling occlusions, self-occlusions, and the limited field of view of the device, which can compromise tracking quality, especially when the subject is in motion or in non-optimal positions. In this work, we propose a novel method for combining data from two synchronized and calibrated Kinect devices to address occlusion and self-occlusion issues, improving tracking and expanding the tracking area. The fusion algorithm is based on the confidence level attributed to the joint's coordinates, the orientation and the distance of the subject from the devices. It was then tested on an acquisition where a subject performed a series of daily activities and simulated a dangerous situation such as falling to the floor. Experimental results demonstrate the effectiveness of fusion algorithm in reducing frame loss and improving the quality of the tracking process, particularly when the subject is between 0.5 and 3 meters from the devices and in a standing or sitting pose. However, when the subject is lying down or positioned beyond the optimal range, the method's effectiveness decreases.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
