  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).
