A Comprehensive Multisensor Dataset Employing RGBD Camera, Inertial Sensor and Web Camera

Soraya, Sabrina I.; Chuang, Shao-Ping; Tseng, Yu–Chee; İk, Tsì-Uí; Ching, Yu-Tai

doi:10.23919/apnoms.2019.8892906

Cited by 4 publications

(1 citation statement)

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“…In [24], the start and the end of an action were synchronized by using the timestamps of the depth images to serve as references due to variations of the frame rate of the Kinect camera and the sampling rate of the wearable inertial sensor. Similarly, in [127], the start and the end of an action were synchronized by using the date/time of the Kinect camera (either RGB images, depth images or skeleton joints) as references. In [128], the data from a Kinect camera and a wearable inertial sensor were collected using C++ codes.…”

Section: A Challenges 1) Data Synchronizationmentioning

confidence: 99%

Vision and Inertial Sensing Fusion for Human Action Recognition: A Review

Majumder

Kehtarnavaz

2021

IEEE Sensors J.

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Human action recognition is used in many applications such as video surveillance, human-computer interaction, assistive living, and gaming. Many papers have appeared in the literature showing that the fusion of vision and inertial sensing improves recognition accuracies compared to the situations when each sensing modality is used individually. This paper provides a survey of the papers in which vision and inertial sensing are used simultaneously within a fusion framework in order to perform human action recognition. The surveyed papers are categorized in terms of fusion approaches, features, classifiers, as well as multimodality datasets considered. Challenges as well as possible future directions are also stated for deploying the fusion of these two sensing modalities under realistic conditions.

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Section: A Challenges 1) Data Synchronizationmentioning

confidence: 99%