Dragonwally is a Stereoscopic Computer Vision Mezzanine for 96Boards CE Boards

Hardware based on 96Boards specifications may not have the number of sales as Raspberry Pi or Orange Pi boards, but there’s heavily used by Linaro member and other developer working on bleeding edge software. More and more companies are designing boards compliant with the standard, and several new mezzanine expansion boards such as Secure96, were showcased at Linaro Connect SFO 2017, and are yet to be show up on 96Boards Mezzanine page. Another 96Boards mezzanine expansion board in development is Dragonwally, designed for stereoscopic computer vision, currently used with DragonBoard 410c board, and targetting applications such as object recognition,  people counting, access control, or driver identification and safety. DragonWally DW0 board specifications: MIPI DSI interface with high speed connector 2x 5MP cameras 1x USB port 96Boards CE compliant The two Brazilian developers working on the project interfaced it with DragonBoard 410c running Linaro Debian, and using OpenCV and Python for computer vision development. To demonstrate the capability of the board, […]

Getting Started with OpenCV for Tegra on NVIDIA Tegra K1, CPU vs GPU Computer Vision Comparison

This is a guest post by Leonardo Graboski Veiga, Field Application Engineer, Toradex Brasil Introduction Computer vision (CV) is everywhere – from cars to surveillance and production lines, the need for efficient, low power consumption yet powerful embedded systems is nowadays one of the bleeding edge scenarios of technology development. Since this is a very computationally intensive task, running computer vision algorithms in an embedded system CPU might not be enough for some applications. Developers and scientists have noticed that the use of dedicated hardware, such as co-processors and GPUs – the latter traditionally employed for graphics rendering – can greatly improve CV algorithms performance. In the embedded scenario, things usually are not as simple as they look. Embedded GPUs tend to be different from desktop GPUs, thus requiring many workarounds to get extra performance from them. A good example of a drawback from embedded GPUs is that they are hardly supported by OpenCV – the de facto standard libraries […]

Embedded Systems Conference 2017 Schedule – May 3-4

The Embedded Systems Conference 2017 will take place over two days in Boston, US on May 3-4, and the organizers have published the schedule of the event. Even if you’re not going to attend, you’ll often learn something or find new information by just checking out the talks and abstracts, so I’ve created my own virtual schedule with some of the most interesting sessions. Wednesday, May 3rd 08:00 – 08:45 – Combining OpenCV and High Level Synthesis to Accelerate your FPGA / SoC EV Application by Adam Taylor, Adiuvo Engineering & Training Ltd This session will demonstrate how you can combine commonly used Open source frameworks such as OpenCV with High Level Synthesis to generate a embedded vision system using FPGA / SoC. The combination of OpenCV and HLS allows for a much faster algorithm development time and consequently a faster time to market for the end application. 09:00 – 09:45 – Understanding the ARM Processor Roadmap by Bob Boys,   Product […]

Open Source ARM Compute Library Released with NEON and OpenCL Accelerated Functions for Computer Vision, Machine Learning

GPU compute promises to deliver much better performance compared to CPU compute for application such a computer vision and machine learning, but the problem is that many developers may not have the right skills or time to leverage APIs such as OpenCL. So ARM decided to write their own ARM Compute library and has now released it under an MIT license. The functions found in the library include: Basic arithmetic, mathematical, and binary operator functions Color manipulation (conversion, channel extraction, and more) Convolution filters (Sobel, Gaussian, and more) Canny Edge, Harris corners, optical flow, and more Pyramids (such as Laplacians) HOG (Histogram of Oriented Gradients) SVM (Support Vector Machines) H/SGEMM (Half and Single precision General Matrix Multiply) Convolutional Neural Networks building blocks (Activation, Convolution, Fully connected, Locally connected, Normalization, Pooling, Soft-max) The library works on Linux, Android or bare metal on armv7a (32bit) or arm64-v8a (64bit) architecture, and makes use of  NEON, OpenCL, or  NEON + OpenCL. You’ll need an […]

JeVois-A33 is a Small Quad Core Linux Camera Designed for Computer Vision Applications (Crowdfunding)

JeVois Neuromorphic Embedded Vision Toolkit – developed at iLab at the University of Southern California – is an open source software framework to capture and process images through a machine vision algorithm, primarily designed to run on embedded camera hardware, but also supporting Linux board such as the Raspberry Pi. A compact Allwinner A33 has now been design to run the software and use on robotics and other projects requiring a lightweight and/or battery powered camera with computer vision capabilities. JeVois-A33 camera: SoC – Allwinner A33  quad core ARM Cortex A7 processor @ 1.35GHz with  VFPv4 and NEON, and a dual core Mali-400 GPU supporting OpenGL-ES 2.0. System Memory – 256MB DDR3 SDRAM Storage – micro SD slot for firmware and data 1.3MP camera capable of video capture at SXGA (1280 x 1024) up to 15 fps (frames/second) VGA (640 x 480) up to 30 fps CIF (352 x 288) up to 60 fps QVGA (320 x 240) up to […]

AVC8000nano mini PCIe Frame Grabber Captures up to 8 D1 Videos

There are plenty of solutions to stream or capture multiple video streams from cameras, but example for security purpose, but usually the equipment is relatively large and heavy. Advanced Micro Peripherals AVC8000nano mini PCIe capture card miniaturizes all that thanks to its form factor, and its 8 u.FL connectors used to capture eight D1 videos at full frame rate. AVC8000nano features: Video Inputs 8x Live NTSC/PAL video inputs with 8x 10-bit ADC and anti-aliasing filters 8x D1 size capture at full frame rate Formats – NTSC-M, NTSC-Japan, NTSC (4.43), RS-170, PAL-B,G,N, PAL-D, PAL-H, PAL-I, PAL-M, PAL-CN, PAL-60 SECAM Adjustments – Contrast, saturation, hue (or chroma phase), and brightness. Software adjustable Sharpness, Gamma and noise suppression Video Capture Formats – RGB555, RGB565, YCbCr 4:2:2, YCbCr 4:1:1 Windows support with Drivers and DirectShow/DirectDraw Linux with drivers and Video4Linux Form factor – Full height mini PCI Express Temperature Range – Commercial: 0°C to 60°C; Extended: –40°C to +85°C The specifications also mentions hardware […]

WRTNode is a Hacker-Friendly Open Source Hardware OpenWRT Wi-Fi Module Selling for $25

There are now some tiny and low cost ($15 to $20) Wi-Fi modules supporting OpenWRT such as VoCore and AsiaRF AWM002. However due to their small size they may not be that hacker’s friendly as they can’t have 2.54mm headers due to heir small size, and I’ve recently received AsiaRF AWM002 only to find out it not only needs 3.3V supply voltage, but also 1.8V and 1.2V. So I’d need to make my own power circuit with the required LDOs, or purchase a $20 base board to use the module. Here comes WRTnode another larger Wi-Fi module but with more usable 2.54mm headers, and based on the more powerful Mediatek MT7620N processor @ 600MHz. WRTnode hardware specifications: Processor – Mediatek  MT7620N 600MHz MIPS CPU (MIPS24KEc) System Memory – 64MB DDR2 Storage – 16MB SPI flash Connectivity – Wi-Fi 2T2R 802.11n 2.4 GHz up to 300Mbps Expansion Headers – 2x with access to  23GPIOs, JTAG, SPI, UART Lite, USB2.0 host USB […]

$192 Nvidia Jetson TK1 Development Board with Tegra K1 Quad Core Cortex A15 SoC

Nvidia has just unveiled Jetson TK1 development kit powered by their 32-bit Tegra K1 quad core Cortex A15 processor with a 192-core Kepler GPU. This board targets computer-vision applications for robotics, medical, avionics, and automotive industries that can leverage the compute capabilities of the Kepler GPU. Jetson TK1 devkit specifications: SoC – Nvidia Tegra K1 SoC with 4-Plus-1 quad-core ARM Cortex A15 CPU, and Kepler GPU with 192 CUDA cores (Model T124) System Memory – 2 GB x16 memory with 64 bit width Storage – 16 GB 4.51 eMMC memory, SATA data + power ports, full size SD/MMC slot, and 4MB SPI boot flash. Video Output – HDMI port Audio – ALC5639 Realtek Audio codec with Mic in and Line out Connectivity – RTL8111GS Realtek GigE LAN USB – 1x USB 2.0 OTG port, micro AB, 1x USB 3.0 port, A Debugging – RS232 serial port, JTAG Expansion 1x Half mini-PCIE slot Expansion port with access to DP/LVDS, Touch SPI […]