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

arXiv: https://arxiv.org/abs/2102.03391 · HTML: https://mlsanity.com/paper/2102.03391/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [Action Tubelet Detector for Spatio-Temporal Action Localization](https://mlsanity.com/paper/1705.01861/) (2017)
- [Finding Action Tubes](https://mlsanity.com/paper/1411.6031/) (2014)
- [Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos](https://mlsanity.com/paper/1703.10664/) (2017)
- [Learning to track for spatio-temporal action localization](https://mlsanity.com/paper/1506.01929/) (2015)
- [Learning Spatiotemporal Features with 3D Convolutional Networks](https://mlsanity.com/paper/1412.0767/) (2014)
- [VideoCapsuleNet: A Simplified Network for Action Detection](https://mlsanity.com/paper/1805.08162/) (2018)
- [REVAMP2T: Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking](https://mlsanity.com/paper/1911.09217/) (2019)

## Abstract (arXiv)

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).