Real-Time Activity Detection of Human Movement in Videos via Smartphone Based on Synthetic Training Data

Rico Thomanek, Tony Rolletschke, Benny Platte, Claudia Hosel, Christian Roschke, Robert Manthey, Manuel Heinzig, Richard Vogel, Frank Zimmer, Matthias Vodel, Maximilian Eibl, Marc Ritter; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2020, pp. 160-164

Abstract


Current research in the domain of video activity detection focuses on real-time activity detection. This includes multiple approaches in the mobile environment, such as the detection of correct motion sequences in the sports and health area or in safety-relevant environments. Current trends focus on the use of 3D CNNs. This work describes a approach to combine the results of a human skeleton point detector with an LSTM on mobile devices. Frameworks for pose detection are combined with LSTMs for activity detection with sensor data, optimized for the mobile area. The resulting system allows the direct detection of a person pose and the classification of activities in a video recorded with a smartphone. The successful application of the system in several field tests shows that the described approach works in principle and can be transferred to a resource-limited mobile environment by optimization.

Related Material


[pdf]
[bibtex]
@InProceedings{Thomanek_2020_WACV,
author = {Thomanek, Rico and Rolletschke, Tony and Platte, Benny and Hosel, Claudia and Roschke, Christian and Manthey, Robert and Heinzig, Manuel and Vogel, Richard and Zimmer, Frank and Vodel, Matthias and Eibl, Maximilian and Ritter, Marc},
title = {Real-Time Activity Detection of Human Movement in Videos via Smartphone Based on Synthetic Training Data},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops},
month = {March},
year = {2020}
}