# Smart Surveillance as an Edge Network Service: from Harr-Cascade, SVM to a Lightweight CNN

arXiv: https://arxiv.org/abs/1805.00331 · HTML: https://mlsanity.com/paper/1805.00331/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2018.

## Built on by (most cited first, 7 of 7)

- [Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things](https://mlsanity.com/paper/2301.03561/) (2023)
- [Anomaly Detection using Edge Computing in Video Surveillance System: Review](https://mlsanity.com/paper/2107.02778/) (2021)
- [Federated Action Recognition on Heterogeneous Embedded Devices](https://mlsanity.com/paper/2107.12147/) (2021)
- [CamPro: Camera-based Anti-Facial Recognition](https://mlsanity.com/paper/2401.00151/) (2023)
- [Anomaly Detection in Residential Video Surveillance on Edge Devices in IoT Framework](https://mlsanity.com/paper/2107.04767/) (2021)
- [LightFuse: Lightweight CNN based Dual-exposure Fusion](https://mlsanity.com/paper/2107.02299/) (2021)
- [A Call to Arms: AI Should be Critical for Social Media Analysis of Conflict Zones](https://mlsanity.com/paper/2311.00810/) (2023)

## Abstract (arXiv)

Edge computing efficiently extends the realm of information technology beyond the boundary defined by cloud computing paradigm. Performing computation near the source and destination, edge computing is promising to address the challenges in many delay-sensitive applications, like real-time human surveillance. Leveraging the ubiquitously connected cameras and smart mobile devices, it enables video analytics at the edge. In recent years, many smart video surveillance approaches are proposed for object detection and tracking by using Artificial Intelligence (AI) and Machine Learning (ML) algorithms. This work explores the feasibility of two popular human-objects detection schemes, Harr-Cascade and HOG feature extraction and SVM classifier, at the edge and introduces a lightweight Convolutional Neural Network (L-CNN) leveraging the depthwise separable convolution for less computation, for human detection. Single Board computers (SBC) are used as edge devices for tests and algorithms are validated using real-world campus surveillance video streams and open data sets. The experimental results are promising that the final algorithm is able to track humans with a decent accuracy at a resource consumption affordable by edge devices in real-time manner.