Active pixels H x V2048 x 1088
Imaging sensorCMOSIS
Resolution (MP)2.2 MP
Spectral optionscolour
Optical format12.7 mm (2/3”)
Active pixels (H x V)5.5 μm
Frame rate (FPS)331 FPS
Dynamic Range 10bit/12bit60 dB
Link to 3D presentation
Product video

T-Rex EVO / 2.2MP C


T-REX EVO is a highly customizable and userprogrammable FPGA-based high-speed smart camera which features a high-end FPGA camera with a Xilinx Zynq FPGA and a highspeed imaging sensor and a Gigabit Ethernet. It includes ARM System-on-Chip (SoC) technology combined with a turbocharged industrial CMOSIS imaging sensor.

With high-performance FPGA System-on-Chip (SoC) technology, the T-REX EVO camera family opens new dimensions in computer vision. It is a global shutter industrial camera with incredible frame rates and an open FPGA architecture. With FPGA processing power the image processing algorithms can run in real time on the camera: just add your imagination.

T-REX EVO incorporates a fully customizable and user-programmable open-reference design for its high-speed FPGA-based camera and application development system. Its emphasis is on an open hardware/software development model, high-frame rates, real-time image processing on FPGA and modern graphical user-interface support.

A suite of intermediate, versatile Xilinx Zynq 7020 FPGAs is used to develop algorithms and process data in real-time. Images are acquired by a CMOSIS sensor, CMV2000 (2048x1088 pixels, 2/3’’ size) or CMV4000 (2048x2048 pixels, 1’’ size). The sensor outputs 760 million pixels per second resulting in 331 FPS (CMV2000) and 176 FPS (CMV4000) at full frame. The onboard 512MB LPDDR2 memory with 3.2GB/s of bandwidth enables usage of complex buffered image processing.

The reference design can be easily edited with standard Xilinx Vivado tools. OptoMotive´s custom IP cores seamlessly integrate inside the Xilinx Vivado toolchain. A large portion of FPGA (PL) is free for the programming and development of new algorithms or the implementation of additional IP cores. The 700MHz Dual Core ARM Cortex A9 Programmable Subsystem runs Linux OS with a custom-made EVO control and streaming stack. User applications or custom data postprocessing can easily be added to the existing design.


  • Laser triangulation - with a ready-made Peak detector with an on-board image processing core;
  • Motion capture - with a ready-made BLOB detector or Running Length Encoder (RLE) on-board image processing core;
  • Industrial process automation - to count, detect, check, verify, read, inspect and test different products, levels, components, etc. at and incredible speed;
  • Industrial quality control - to inspect defects, cracks or surface blemishes, size, position, dimension and color, foreign objects or quality and
  • General R&D.
ADC Resolution10 bit
Analogue Gain1 - 3.2x
Region of InterestYES, with 8 pixel increments
Shutter TypeElectronic global shutter
Shutter Time2.4 us – 90 s
Pixel Clock Speed760 MHz (8 pixels @ 95 MHz)
ExposureLinear, 3 slope high dynamic range
Pixel CorrectionDead pixel correction and programmable LUT
Trigger ModesFree running, trigger, overlap, pulse width
Trigger FeaturesDelay 0 – 1000 ms, LP Filter 1.5Hz - 100 kHz
Shutter Resolution21 ns
FPGAZynq 7020
Free FPGA %Up to 50%, most of the 220 slices of DSP are free.
Volatile Memory512MB LPDDR2
Non-volatile Memory32MB QSPI flash, optional eMMC
Lens MountC-mount (1” 32G thread)
Temp Range0 - 50°C
Mass50 g OEM / 290 g with housing
ProtectionUp to IP67 with housing
Housing MaterialCNC-machined aluminum, anodized in a special OptoMotive blue color
RoHSRoHS compliant
Fixing Holes4x M3 OEM / 5 x M6 on housing
Input VoltagePower over Ethernet 42-57V or 5V (OEM)
Consumptionup to 11W
IO IsolationNo, but camera has 1.5kV PoE isolation
ConnectorsRJ45, 4 pin LEMO EXG 00 304
On-board Image ProcessingAs an option (if an IP Core is integrated)
Open Reference DesignYes
Open architectureYes
SoftwareCompatible with OptoMotive EVO software (full source included)
Operating SystemWindows 7, Windows 10, 64bit or 32bit
Development ToolsXilinx Vivado/SDK 2017.2 Microsoft Visual Studio 2017