Real-time Object and Event Detection Service through Computer Vision and Edge Computing

TL;DR

The paper tackles real-time road safety in smart cities using a computer-vision pipeline deployed at the network edge on a single static camera. By integrating camera calibration, geo-framing, and a YOLOv8n-based detection module, it derives ground-plane positions and inter-object distances to infer collision risk, all implemented across edge nodes within the Aveiro ATCLL testbed. Key contributions include a low-cost, low-latency single-camera approach that provides distance estimates, velocity/acceleration cues, and a collision-prediction mechanism (with Ras/Rad road-state metrics) suitable for near real-time urban safety monitoring. The results demonstrate a median end-to-end latency of $0.44$ s and distance validations around $8$ m crosswalks, highlighting practical potential for proactive safety interventions in smart-city environments.

Abstract

The World Health Organization suggests that road traffic crashes cost approximately 518 billion dollars globally each year, which accounts for 3% of the gross domestic product for most countries. Most fatal road accidents in urban areas involve Vulnerable Road Users (VRUs). Smart cities environments present innovative approaches to combat accidents involving cutting-edge technologies, that include advanced sensors, extensive datasets, Machine Learning (ML) models, communication systems, and edge computing. This paper proposes a strategy and an implementation of a system for road monitoring and safety for smart cities, based on Computer Vision (CV) and edge computing. Promising results were obtained by implementing vision algorithms and tracking using surveillance cameras, that are part of a Smart City testbed, the Aveiro Tech City Living Lab (ATCLL). The algorithm accurately detects and tracks cars, pedestrians, and bicycles, while predicting the road state, the distance between moving objects, and inferring on collision events to prevent collisions, in near real-time.

Figures (10)

• Figure 1: System architecture diagram.
• Figure 2: System latency measurement diagram.
• Figure 3: System latency cdf and pdf.
• Figure 4: GStreamer latency cdf and pdf.
• Figure 5: Frame calibration and tracking object.