Alert360: A Low-Cost, Interpretable Browser-Based Motion Awareness for Multi-Camera Monitoring

Abstract

This paper presents Alert360, a low-cost and intrinsically interpretable browser-based multi-camera motion awareness system for local monitoring in security, retail, education, laboratory, and workplace settings, designed to run on commodity cameras and consumer-grade browser-capable computers without dedicated GPUs. Existing video analytics platforms are often cloud-dependent, costly, or designed for complex surveillance tasks that exceed the needs of small sites. Alert360 addresses a narrower but practically important problem: lightweight operational motion awareness using commodity cameras and modern web browsers. Alert360 provides lightweight, rule-based, and privacy-conscious motion awareness using commodity cameras and modern web browsers. The system performs camera capture, frame processing, motion estimation, alerting, and event review entirely on the client side. Its motion pipeline includes frame down-scaling, grayscale conversion, smoothing, adaptive background differencing, temporal differencing, mask refinement, and blob-based activity estimation. The interface supports concurrent camera streams, configurable frame rates, adjustable sensitivity, movement focus, off-screen guidance, optional acoustic alerts, and local snapshot retention. A controlled benchmark across seven multi-camera scenarios showed a mean processing time of 10.62 ms per frame, zero late frames, and a maximum estimated processing duty cycle of 24.43%. An illustrative office-area deployment using a 1080p 360-degree webcam showed that all thirty scripted human-movement scenarios activated the expected motion-triggered recording workflow. These results demonstrate that motion awareness can be delivered through browser-native local processing on consumer-grade computers under the evaluated multi-camera configurations.