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2021 | OriginalPaper | Chapter

Single Run Action Detector over Video Stream - A Privacy Preserving Approach

Authors : Anbumalar Saravanan, Justin Sanchez, Hassan Ghasemzadeh, Aurelia Macabasco-O’Connell, Hamed Tabkhi

Published in: Deep Learning for Human Activity Recognition

Publisher: Springer Singapore

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Abstract

This paper takes initial strides at designing and evaluating a vision-based system for privacy ensured activity monitoring. The proposed technology utilizing Artificial Intelligence (AI)-empowered proactive systems offering continuous monitoring, behavioral analysis, and modeling of human activities. To this end, this paper presents Single Run Action Detector (S-RAD) which is a real-time privacy-preserving action detector that performs end-to-end action localization and classification. It is based on Faster-RCNN combined with temporal shift modeling and segment based sampling to capture the human actions. Results on UCF-Sports and UR Fall dataset present comparable accuracy to State-of-the-Art approaches with significantly lower model size and computation demand and the ability for real-time execution on edge embedded device (e.g. Nvidia Jetson Xavier).

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previous chapter ARID: A New Dataset for Recognizing Action in the Dark

next chapter Efficacy of Model Fine-Tuning for Personalized Dynamic Gesture Recognition

https://github.com/TeCSAR-UNCC/S-RAD-ActionLocalizationClassification.

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Title: Single Run Action Detector over Video Stream - A Privacy Preserving Approach
Authors: Anbumalar Saravanan
Justin Sanchez
Hassan Ghasemzadeh
Aurelia Macabasco-O’Connell
Hamed Tabkhi
Publisher: Springer Singapore
Book: Deep Learning for Human Activity Recognition
Print ISBN: 978-981-16-0574-1

Electronic ISBN: 978-981-16-0575-8

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-981-16-0575-8_7

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