Multi-camera tracking module

The revolutionary way to fully understand vehicle behaviour

Connection of multiple fields of view for accurately reconstructing the complete trajectory of people and vehicles across a monitored area.

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multi-camera tracking module

The Multi-Camera Tracking Module drastically increases the potential of Sprinx deep learning-based video analytics for vehicle and people mobility, such as traffix.ai and co.exist, in tunnels and intersections.

The activation of this Module enables the connection of multiple fields of view to reconstruct the complete trajectory of people and vehicles and make mobility even safer, smarter, more sustainable, and efficient.

Acting like a 'SUPERCAMERA'

The objects that move in the field of view of different cameras are correlated through their trajectories. The Multi-Camera Tracking Module allows reconstruction of their path within the survey area. In this way, the single behaviour of a pedestrian or a vehicle within a single shot could be expected, with a global and comprehensive vision that could bring out an anomaly of movement, thus generating action scenarios for control room operators.
The Multi-Camera Tracking Module completely revolutionizes the perspective with which vehicle behaviour is analyzed and attributes greater effectiveness and efficiency to traffic monitoring systems.

A video analytics system is currently based on analysing information detected by individual cameras. Therefore, every video stream processed by the video analysis system is independent of the other cameras, even if they are contiguous and/or partially frame the same scenario. The Multi-Camera Tracking Module introduces a disruptive innovation, allowing the “fusion” of individual information. For the video analysis system, the survey area (tunnel, roundabout, intersection, a stretch of road, etc.) is not only the sum of individual views independent from the others. Still, it is a complete and global view reconstructed from the software.

The Multi-Camera Tracking Module can be activated as an add-on of both traffix.ai and co.exist platforms. In the first case, the purpose is further to increase the degree of safety inside a tunnel. In the second case, the add-on guarantees more efficient intersections and roundabouts in cities and towns.

Key Features

Supercamera View

Comprehensive monitoring of individuals and vehicles across large areas that cannot be entirely covered by a single camera ensures effective wide-area surveillance.

Self-Calibration

The Module enables Sprinx AI video analysis systems to generate a global model of roads, tunnels, roundabouts, and intersections, reconstructing perspective.

Safety

Detecting anomalous behaviours within the road section, such as sudden decelerations or distraction while driving, increasing the degree of safety and efficiency in traffic monitoring.

Robustness

In case of tampering or the simple maintenance of the CCTV system, the Sprinx AI-based video analysis platform recalibrates itself
automatically.

Reliability

In case of a tunnel accident, the system accurately provides geolocation and indicates the exact number of vehicles inside, categorized according to their specific class.

AI based

The combination of 3D object tracking and Deep-Learning mosule, drastically increases the degree of efficiency and effectiveness of mobility analysis systems, inside tunnels and outdoor.