Neural networks target traffic management

SEPTEMBER 17--RC Module (Moscow, Russia) has introduced a neural-network-based traffic-monitoring system, the NeuroMatrix Traffic Monitor-E. The system uses a neural-network IC to perform image correction, segmentation, tracking, classification, and measurement.

SEPTEMBER 17--RC Module (Moscow, Russia) has introduced a neural-network-based traffic-monitoring system, the NeuroMatrix Traffic Monitor-E. The system uses a neural-network IC to perform image correction, segmentation, tracking, classification, and measurement. At the heart of the system is a stand-alone embedded imaging system that does not require an external PC or host computer.

Once PAL or NTSC images are captured and stored, the system uses known camera height and ground coordinates to calculate the relationship between screen and road coordinates. After this calibration has been performed, static and motion filters running on the system's on-board NM6403 neural-network processor detect every vehicle as it moves into and through the system's field of view.

In addition to performing statistical analysis on up to six lanes of highway traffic, the system can recognize up to five classes of vehicles, according to Dmitri Fomine, director of marketing and sales at RC Module. Computed statistical data such as vehicle flow rates, types of vehicles, lane occupancies, and average speeds can be transferred to other PC-based systems over an RS-232 data port. In this way, several systems can be linked to provide a networked traffic management system.

For more on this story, see the September issue of Vision Systems Design.

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