Sprinx TRAFFIX (formerly SX-TRAFFIC) is a complete and professional video-based solution for Automatic Incident Detection and Traffic Data Collection. It allows reliable detection of incidents and anomalies in traffic flow on critical infrastructures such as highways, tunnels and intersections.
TRAFFIX - Video-based solution for Automatic Incident Detection and Traffic Data Collection
Based on advanced Object Tracking and Deep Learning algorithms, Sprinx TRAFFIX is a software solution that allows reliable detection of incidents and anomalies in traffic flow on critical infrastructures such as highways, tunnels and intersections.
TRAFFIX enables to quickly alert operators about incidents and traffic slowdown, to promptly send notifications to Milestone XProtect VMS platform and automatically place a bookmark in the recorder footage or trigger any action.
TRAFFIX allows the collection of data related to traffic flow. It allows smart city officers to get mobility insights regarding distribution and composition of the traffic.
TRAFFIX is more than a standard AID software. In addition to analyzing images from standard IP cameras (ONVIF Profile S compliant) to detect traffic events, it can collect alarms and data processed by Sprinx TRAFFIC APPs and license plate information from ANPR cameras, becoming a real traffic platform.
Traffic Event Detection:
Stopped Vehicle, Slowdown&Queue, Pedestrian, Wrong Way, Loss of Visibility, Spilled Cargo, Slow/Fast Vehicle, Lane Change
Traffic Data Collection:
Vehicle Counting and Classification, Average Speed, Average Distance between vehicles, Traffic Density, O/D Matrix
Easy integration with traffic systems through: TCP messages, OPC-UA protocol, Modbus TCP, dry contacts (Moxa IO device)
Web dashboard to visualize clear graphics of the traffic data and to navigate through the event journal with the snapshot and the video of each alarm.
By leveraging powerful Deep Neural Networks, TRAFFIX AI analyses video streams in real time to quickly classify vehicles and identify traffic issues.