# Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things

arXiv: https://arxiv.org/abs/2301.03561 · HTML: https://mlsanity.com/paper/2301.03561/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 74 references that are in the map.
- First posted on arXiv 9 Jan 2023.

## Builds on (most cited first, 29 of 29)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://mlsanity.com/paper/2211.05778/) (2022)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2107.12213/) (2021)
- [Omni-Scale Feature Learning for Person Re-Identification](https://mlsanity.com/paper/1905.00953/) (2019)
- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [Peeking into the Future: Predicting Future Person Activities and Locations in Videos](https://mlsanity.com/paper/1902.03748/) (2019)
- [Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos](https://mlsanity.com/paper/1903.03295/) (2019)
- [Graph Embedded Pose Clustering for Anomaly Detection](https://mlsanity.com/paper/1912.11850/) (2019)
- [Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing](https://mlsanity.com/paper/2001.00964/) (2020)
- [PYSKL: Towards Good Practices for Skeleton Action Recognition](https://mlsanity.com/paper/2205.09443/) (2022)
- [Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization](https://mlsanity.com/paper/2103.15537/) (2021)
- [On the Unreasonable Effectiveness of Centroids in Image Retrieval](https://mlsanity.com/paper/2104.13643/) (2021)
- [MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection](https://mlsanity.com/paper/2012.00914/) (2020)
- [A Comparative Review of Recent Kinect-based Action Recognition Algorithms](https://mlsanity.com/paper/1906.09955/) (2019)
- [Crowded Scene Analysis: A Survey](https://mlsanity.com/paper/1502.01812/) (2015)
- [A Smart, Efficient, and Reliable Parking Surveillance System with Edge Artificial Intelligence on IoT Devices](https://mlsanity.com/paper/2001.00269/) (2020)
- [Understanding bias in facial recognition technologies](https://mlsanity.com/paper/2010.07023/) (2020)
- [REVAMP2T: Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking](https://mlsanity.com/paper/1911.09217/) (2019)
- [Smart Surveillance as an Edge Network Service: from Harr-Cascade, SVM to a Lightweight CNN](https://mlsanity.com/paper/1805.00331/) (2018)
- [Anomaly Detection using Edge Computing in Video Surveillance System: Review](https://mlsanity.com/paper/2107.02778/) (2021)

## Built on by (most cited first, 17 of 17)

- [An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction](https://mlsanity.com/paper/2406.15395/) (2024)
- [Understanding the Challenges and Opportunities of Pose-based Anomaly Detection](https://mlsanity.com/paper/2303.05463/) (2023)
- [CREW: Facilitating Human-AI Teaming Research](https://mlsanity.com/paper/2408.00170/) (2024)
- [Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety](https://mlsanity.com/paper/2302.04310/) (2023)
- [A Survey on Human-AI Collaboration with Large Foundation Models](https://mlsanity.com/paper/2403.04931/) (2024)
- [Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark](https://mlsanity.com/paper/2408.14329/) (2024)
- [Human-Centric Video Anomaly Detection Through Spatio-Temporal Pose Tokenization and Transformer](https://mlsanity.com/paper/2408.15185/) (2024)
- [From Lab to Field: Real-World Evaluation of an AI-Driven Smart Video Solution to Enhance Community Safety](https://mlsanity.com/paper/2312.02078/) (2023)
- [Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and Benchmark](https://mlsanity.com/paper/2501.06591/) (2025)
- [MoFM: A Large-Scale Human Motion Foundation Model](https://mlsanity.com/paper/2502.05432/) (2025)
- [Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility](https://mlsanity.com/paper/2303.15495/) (2023)
- [A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment](https://mlsanity.com/paper/2508.14203/) (2025)
- [From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Security](https://mlsanity.com/paper/2603.04723/) (2026)
- [MoCLIP: Motion-Aware Fine-Tuning and Distillation of CLIP for Human Motion Generation](https://mlsanity.com/paper/2505.10810/) (2025)
- [Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild](https://mlsanity.com/paper/2603.04727/) (2026)
- [Real-World Community-in-the-Loop Smart Video Surveillance -- A Case Study at a Community College](https://mlsanity.com/paper/2303.12934/) (2023)
- [VegaEdge: Edge AI Confluence Anomaly Detection for Real-Time Highway IoT-Applications](https://mlsanity.com/paper/2311.07880/) (2023)

## Abstract (arXiv)

With the advancement of vision-based artificial intelligence, the proliferation of the Internet of Things connected cameras, and the increasing societal need for rapid and equitable security, the demand for accurate real-time intelligent surveillance has never been higher. This article presents Ancilia, an end-to-end scalable, intelligent video surveillance system for the Artificial Intelligence of Things. Ancilia brings state-of-the-art artificial intelligence to real-world surveillance applications while respecting ethical concerns and performing high-level cognitive tasks in real-time. Ancilia aims to revolutionize the surveillance landscape, to bring more effective, intelligent, and equitable security to the field, resulting in safer and more secure communities without requiring people to compromise their right to privacy.