YUTELA: A Scalable Edge Cloud AIoT System for Multi-Site Video Anomaly Detection with Scene Specific Adaptation

Shanle Yao; Babak Rahimi Ardabili; Amir Kimiyaie; Hamed Tabkhi

cs.CVOct 19, 2026

YUTELA: A Scalable Edge Cloud AIoT System for Multi-Site Video Anomaly Detection with Scene Specific Adaptation

Authors: Shanle Yao, Babak Rahimi Ardabili, Amir Kimiyaie, Hamed Tabkhi

Organizations: The authors are with the Electrical and Computer Engineering Department, The University of North Carolina at Charlotte, Charlotte, NC, 28223 USA.

Abstract

Building upon our previous Ancilia framework, this work presents YUTELA, an edge--server--cloud AIoT system for long-term, multi-site video anomaly detection. YUTELA replaces workstation-based on-site processing with NVIDIA Jetson Thor edge devices and redesigns the multi-camera pipeline through camera batching and redistribution, shared memory, reduced inter-stage data transfer, task integration, and TensorRT-accelerated inference. It maintains a separate anomaly model per camera, retrained on a centralized GPU server once enough recent pseudo-normal data are collected, with updated weights returned to the edge. With eight cameras, TensorRT increases throughput from 14.17 to 29.94 FPS under normal density, and the optimized edge node exceeds the throughput of the workstation-based Ancilia system in seven of nine workload settings, reaching up to 2.03 times its throughput at extreme density. This gain comes at the cost of higher latency under lighter loads with four or eight streams. YUTELA was then operated continuously for 170 hours at four public sites using 31 existing CCTV cameras. The deployment shows that runtime load is driven by scene activity and input frame rate rather than by camera count, and that retraining frequency varies by about three times across sites. The 101 automatically triggered training events occupied only about 6.6% of a single GPU, indicating that one global node can support adaptation for tens of sites. Mean end-to-end alert latency ranged from 6.8 to 15.4~s depending on site traffic and anomaly type. These results show that YUTELA enables practical multi-camera edge processing with low-cost, camera-specific adaptation across heterogeneous sites.

Figures & tables

Fig. 1: YUTELA system architecture and data flow. $N$ distributed edge nodes perform real-time video analysis and interact with a global node for scene-specific anomaly adaptation and multi-site statistical analysis, including behavior anomaly information, while anomaly information is transferred to the cloud for alerting and user interaction. Multiple deployment sites may share the global and cloud infrastructure, although one group is shown for clarity. $BB_{P}$ , $ID_{P}$ , $KP$ , $AS$ , $BB_{O}$ , $D$ , $SSTD$ , $WW$ , $I$ , $SA$ , $A$ , $UI$ , $R$ , and $FB$ denote pedestrian bounding boxes, identities, keypoints, anomaly scores, object-anomaly bounding boxes, batched data, scene-specific trainable data, updated weights, database information, statistical analysis, alerts, user-interface information, requests, and feedback, respectively.

Task	Method	Performance	Dataset
High-Accuracy Reference Algorithms
Object Detection	Co-DETR [ 42 ]	66.0 (AP)	COCO [ 43 ]
Tracking	HyperSSM [ 44 ]	80.0 (MOTA)	MOT20 [ 45 ]
Pose Estimation	ViTPose [ 46 ]	81.1 (AP)	COCO [ 43 ]
Anomaly Detection	SPARTA [ 30 ]	64.9 (AUC-ROC)	HuVAD [ 41 ]
Ancilia Algorithmic Core [ 4 ]

TABLE I: Comparison of high-accuracy reference models and the algorithmic cores of YUTELA.

Fig. 2: YUTELA edge processing and scene-specific adaptation architecture. Batched multi-camera streams share edge memory and inference resources, while anomaly features are collected at the global node for scene-specific model retraining.

Fig. 3: Representative camera views from the four real-world YUTELA deployment sites, including a university campus, local market, downtown restaurant, and art center. Two camera views are shown for each site to illustrate differences in scene layout, viewpoint, and deployment environment.

Fig. 4: Edge-node throughput (top) and end-to-end pipeline latency (bottom) as the number of concurrent camera streams increases, under normal (70 detections/s), heavy (216 detections/s), and extreme (744 detections/s) crowd density. The Ancilia workstation (Threadripper Pro 3975WX, three A6000 GPUs) is compared with YUTELA on a single Jetson Thor, with and without TensorRT.

Site	Deployment			Runtime Performance			Triggered Continual Training
Site	Cams.	Env.	Input FPS	Peak Flow	Min Proc. FPS /Cam.	Max Lat. (s) /Cam.	Total Triggers	Triggers /Cam.	Inter-Trigger Interval (h)	Train Time /Event (min)
Site A	8	Urban Campus	30	29	10.96	16.71	9	0–3	55.0 ± 20.4	4.2 ± 2.4
Site B	7	Dining Complex	30	63	2.98	37.38	59	6–10	19.4 ± 4.6	7.7 ± 4.4
Site C	8	Public Marketplace	15	77	2.78	28.03	20	1-4	42.2 ± 6.3	6.2 ± 3.4
Site D	8	Center	20	30	1.92	15.98	13	0-5	41.0 ± 12.8	4.6 ± 3.1

TABLE II: Multi-site deployment, runtime performance, and triggered continual-training statistics.

Panel of Figure 7 — Fig. 5: Runtime behavior of YUTELA during the continuous 170-hour deployments at the four sites. In each panel, the upper plot shows processing throughput (FPS) and pipeline latency, and the lower plot shows the number of people detected across the site’s cameras.

System	Location	Traffic Flow	Anomaly	Mean $\pm$ Std. (s)	Range (s)
Ancilia [ 8 ]	Location B	0–10	Object	$8.92\pm 0.71$	7.46–10.61
Ancilia [ 8 ]	Location B	0–10	Behavior	$11.62\pm 0.62$	10.75–13.30
YUTELA	Site A	0–10	Object	$6.82\pm 0.57$	5.70–7.69
	Site A	0–10	Behavior	$8.71\pm 0.63$	7.87–10.27
	Site B	20–30	Object	$11.33\pm 0.69$	9.98–12.39
	Site B	20–30	Behavior	$15.41\pm 0.74$	13.96–16.54

TABLE III: Physical-Cyber-Physcial alert-trigger latency for object and behavior anomalies with eight concurrent surveillance cameras under different traffic flows. The Ancilia baseline is reported from [ 8 ] .

Video Anomaly Detection

Video anomaly detection identifies unusual events or behaviors in video, often when examples of the anomalies are scarce or unknown.

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Abstract

Video Anomaly Detection

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