Frequently Asked Questions
Processor: Allwinner A33 quad core ARM Cortex A7 processor @ 1.34GHz with VFPv4 and NEON, and a dual core Mali-400 GPU supporting OpenGL-ES 2.0.
Memory: 256MB DDR3 SDRAM running at full DDR3-1600 speed.
Solid state disk: micro SD slot for firmware and data, supports up to 25Mbyte/s.
Camera sensor: 1.3MP camera with
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 60 fps
QCIF (176 x 144) up to 120 fps
QQVGA (160 x 120) up to 60 fps
QQCIF (88 x 72) up to 120 fps
USB device interface: mini USB port for power, video streaming (UVC webcam standard), and serial-over-USB command-line interface and text data streaming.
Hardware serial port: 5V or 3.3V (selected through VCC-IO pin) micro serial port connector to communicate with Arduino or other embedded controllers.
Power: 3.5 Watts maximum from USB port. Requires USB 3.0 port or Y-cable to two USB 2.0 ports.
Thermal: Integrated cooling fan, maintain stable 1.34GHz speed under full processor load, no overheating or thermal throttling.
LED: One two-color LED: Green: power is good. Orange: power is good and camera is streaming video frames.
Size: 28 cc or 1.7 cubic inches (plastic case included with 4 holes for secure mounting).Last updated:
Working machine vision modules (as shown in the video) currently include:
Visual attention: find interesting things.
Gist: recognize different places.
Deep neural networks: Read digits, recognize objects using neural nets.
ArUco: detect &decode simple patterns.
QRcode and barcode: Detect & decode.
Background subtraction: find moving things.
GPU image processing examples.
NEON-accelerated image processing example (blur filter runs 6x faster using NEON multimedia processor instructions compared to standard CPU instructions).
Eye tracking: Build your own 120Hz eye tracker.
Features: Dense SIFT feature extraction.
Multicore processing: Quad-core edge detection demo. Almost all modules exploit multicore technology.
Coler tracking: Detect and track objects by their color.
Motion flow detection: Fast optical flow.
utonomous driving: Detect and follow roads for autonomous cars.
Object matching: Detect and recognize objects using SURF key points.
Image segmentation: Segment images into superpixels.
Video capture: Save video to microSD for later analysis.
Passthrough: Use your JeVois camera as a standard USB camera capable of up to 120 frames/s.
Many more to come, share your own via our web portal, download others’Last updated:
All software open source (GPL)
Full Linux operating system, boots in 5 seconds
Custom kernel modules for camera & USB
Buildroot framework to easily add software packages and create SD image
JeVois C++17 video capture, processing & streaming framework
Switch machine vision modules on the fly by changing output resolution
Download pre-programmed machine vision modules or create your own
CMake build system
Full cross-compiler suite (compile all software on your desktop)
Compile and run the same software on desktop and on JeVois hardware, at the same time (very useful for development & debugging)
Operating system and vision software all stored on microSD card, hacker-friendly and unbrickable. Smart camera can use microSD to save data.
Included software libraries (used for the demos seen in the video):
Neuromorphic algorithms for visual attention & scene understanding
OpenCV 3.1 machine vision algorithms
All opencv-contrib modules (object recognition, ArUco, etc)
ZBar library for barcode & QRcode detection and decoding
tiny-dnn library for deep convolutional neural networks
GPU-accelerated image processing using OpenGL ES2.0 shaders
Support for NEON multimedia processor instructions
Vlfeat library for visual feature computation
OF_DIS library for fast motion flow computation
Eigen3, TBB, OpenMP, etcLast updated:
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