# Anomaly Detection in Residential Video Surveillance on Edge Devices in IoT Framework

arXiv: https://arxiv.org/abs/2107.04767 · HTML: https://mlsanity.com/paper/2107.04767/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 10 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Distributed Deep Neural Networks over the Cloud, the Edge and End Devices](https://mlsanity.com/paper/1709.01921/) (2017)
- [Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey](https://mlsanity.com/paper/1901.08292/) (2019)
- [SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems](https://mlsanity.com/paper/1909.09709/) (2019)
- [Smart Surveillance as an Edge Network Service: from Harr-Cascade, SVM to a Lightweight CNN](https://mlsanity.com/paper/1805.00331/) (2018)
- [Good Features to Correlate for Visual Tracking](https://mlsanity.com/paper/1704.06326/) (2017)
- [Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras](https://mlsanity.com/paper/1810.04108/) (2018)

## Built on by (most cited first, 1 of 1)

- [Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection System](https://mlsanity.com/paper/2307.16834/) (2023)

## Abstract (arXiv)

Intelligent resident surveillance is one of the most essential smart community services. The increasing demand for security needs surveillance systems to be able to detect anomalies in surveillance scenes. Employing high-capacity computational devices for intelligent surveillance in residential societies is costly and not feasible. Therefore, we propose anomaly detection for intelligent surveillance using CPU-only edge devices. A modular framework to capture object-level inferences and tracking is developed. To cope with partial occlusions, posture deformations, and complex scenes, we employed feature encoding and trajectory association governed by two metrices complementing to each other. The elements of an anomaly detection framework are optimized to run on CPU-only edge devices with sufficient frames per second (FPS). The experimental results indicate the proposed method is feasible and achieves satisfactory results in real-life scenarios.