Intelligent Video Analytics for Safety and Traffic Management in Smart Cities
The problems were evident: traffic incidents (accidents, congestion, vehicles driving the wrong way, speeding) took minutes or hours to detect. Situations endangering citizens (robberies, fights, fallen people, dangerous crowding) were frequently identified only after they had already occurred, through review of recordings. Emergency services' response times were high because detection was late or depended on calls from citizens.
The city council needed to turn its camera network into an intelligent, proactive system for safety and urban management.
Traffic incident detection: Computer vision models trained to detect in real time: accidents and collisions, vehicles stopped on expressways, speeding through flow analysis, vehicles driving the wrong way, pedestrians crossing outside crosswalks in risk areas, and traffic congestion with propagation prediction.
Public safety: Algorithms specialized in detecting: fights and assaults (analysis of aggressive movement patterns), robberies and thefts (detection of suspicious behaviors such as sudden dashes and chases), people fallen or motionless on the ground for a prolonged period, dangerous crowds exceeding density thresholds, and abandoned objects in sensitive areas.
Edge + Cloud architecture: Edge processing devices installed alongside groups of cameras perform the primary analysis in real time, reducing latency to under 500ms. Only relevant events are sent to the cloud for validation with more complex models, classification and storage.
Intelligent command center: A centralized dashboard displays all detected events in real time on an interactive map of the city, with priority levels and automatic notifications to the Local Police, emergency services and traffic managers depending on the type of incident.
67% reduction in response times: Emergency services receive automatic alerts with the exact location and type of incident in less than 30 seconds from detection, compared to the previous 8-15 minutes.
92% of traffic incidents detected: The system automatically identifies the vast majority of road incidents, enabling proactive traffic management.
41% reduction in crime in monitored areas: Early detection and rapid police intervention have had a significant deterrent effect.
Predictive traffic management: Analysis of historical patterns makes it possible to anticipate congestion and activate preventive detours, reducing traffic jams by 28%.
Proven scalability: The system was initially deployed on 500 cameras and has been progressively extended to all 3,000, validating the edge + cloud architecture at large scale.
Computer Vision / Deep Learning
Edge + Cloud Architecture
Real-Time Command Center