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

• URL: http://arxiv.org/abs/2102.03391v1
• Date: Fri, 5 Feb 2021 19:27:38 GMT
• Title: Single Run Action Detector over Video Stream -- A Privacy Preserving Approach
• Authors: Anbumalar Saravanan, Justin Sanchez, Hassan Ghasemzadeh, Aurelia Macabasco-O'Connell and Hamed Tabkhi
• Abstract summary: This paper presents Single Run Action Detector (S-RAD) which is a real-time privacy-preserving action detector. Results on UCF-Sports and UR Fall dataset present comparable accuracy to State-of-the-Art approaches.
• Score: 13.247009439182769
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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