Skip to main content

New Atalaya products

Spanish company Imagsa Technologies has unveiled several new products in its Atalaya range of traffic cameras. For instance, the Atalaya3D is an innovative high-speed stereoscopic camera that uses parallel computing techniques to successfully perform real-time three-dimensional analysis of road traffic. It provides, in a single unit, a wide range of traffic measurements, such as precise speed and inter-distance measurement or vehicle counting and classification, combining applications as diverse as speed en
June 19, 2012 Read time: 2 mins
Spanish company 65 Imagsa Technologies has unveiled several new products in its Atalaya range of traffic cameras. For instance, the Atalaya3D is an innovative high-speed stereoscopic camera that uses parallel computing techniques to successfully perform real-time three-dimensional analysis of road traffic. It provides, in a single unit, a wide range of traffic measurements, such as precise speed and inter-distance measurement or vehicle counting and classification, combining applications as diverse as speed enforcement, journey time monitoring or dangerous goods transport control.

Imagsa has also launched AtalayaCompact, an autonomous (no external trigger) megapixel smart camera to perform ALPR in real time and with top accuracy under challenging traffic, light and weather conditions. It integrates a high-speed megapixel CMOS sensor (250fps) and a supercomputing device (FPGA), to detect and analyse license plates in real time.

AtalayaSpeed+Class is a software module that is used with the AtalayaCompact ALPR camera for speed and classification. According to Imagsa, this enables a great variety of traffic measurements in a single ALPR camera, without requiring any additional investments on inductive loops, radars or lasers.

Another product from Imagsa is MercuryTraffic, an ultra-compact license plate sensor that combines a megapixel high-speed CMOS sensor (able to capture up to 1,000 images per second) with high-performance hardware performing advanced image processing algorithms in real time. The company says that license plate images provided by MercuryTraffic enable OCR software to achieve maximum recognition rates.

Related Content

  • May 18, 2018
    New ANPR solutions overcome variables
    The sheer range of variables makes it difficult to find a single algorithm to ensure a 100% standard of ANPR. David Crawford investigates new processing technology. Automatic number plate recognition (ANPR), using optical character recognition and image-processing to identify vehicles, plays key roles in traffic monitoring and law enforcement, access and parking control, electronic toll collection, vehicle security and crime deterrence. Overall, system performance is well rated, with high levels of
  • November 20, 2013
    Bluetooth and Wi-Fi offer new options for travel time measurements
    New trials show Bluetooth and Wi-Fi signals can be reliably used for measuring travel times and at a lower cost than an ANPR system, but which is the better proposition depends on many factors. Measuring travel times has traditionally relied automatic number plate (or licence plate) recognition (ANPR/ALPR) cameras capturing the progress of vehicles travelling along a pre-defined route. Such systems also have the benefit of being able to count passing traffic and have become a vital tool in dealing with c
  • March 12, 2025
    Tattile OCR system for Myanmar tolling
    Stop-and-go system uses embedded optical character recognition cameras
  • January 25, 2012
    Machine vision - cameras for intelligent traffic management
    For some, machine vision is the coming technology. For others, it’s already here. Although it remains a relative newcomer to the ITS sector, its effects look set to be profound and far-reaching. Encapsulating in just a few short words the distinguishing features of complex technologies and their operating concepts can sometimes be difficult. Often, it is the most subtle of nuances which are both the most important and yet also the most easily lost. Happily, in the case of machine vision this isn’t the case: