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OpenCV 4.5 simple optical flow GPU tutorial cuda::FarnebackOpticalFlow

This OpenCV tutorial is a very simple code example of GPU Cuda optical flow in OpenCV written in c++. The configuration of the project, code, and explanation are included for farneback Optical Flow method. Farneback algorithm is a dense method that is used to process all the pixels in the given image. The dense methods are slower but more accurate as all the pixels of the image are processed. In the following example, I am displaying just a few pixes based on a grid. I am not displaying all the pixes. In the opposite to dense method the sparse method like Lucas Kanade using just a selected subset of pixels. They are faster. Both methods have specific applications. Lucas-Kanade is widely used in tracking. The farneback can be used for the analysis of more complex movement in image scene and furder segmentation based on these changes. As dense methods are slightly slower, the GPU and Cuda implementation can lead to great performance improvements to calculate optical flow for all pixels o

Internet of computer vision things Movidius

About Movidius

Share this for more tutorials and computer vision post from me.. Thanks best Vladimir


Movidius Computer Vision


Movidius is interesting company which developed embedded machine vision technology for new segment of fast growing smartphones and IoT apps. Smart devices in internet of things needs smart processing units like this for 3D depth application, reconstruction, detection and Natural user interface. Near future brings challenging task like smart mirror and communication walls and many more "disturbing things". Movidius provides optimized library for algorithm in modern deep learning in combination with low-power visual processing units. 

Target Computer vision app 

  • 3D modeling and reconstruction in smart devices of eye of thinks apps
  • In door navigation assistant
  • Visual analysis 
  • Augmented reality
  • Recognition and classification apps

Market 










Movius Vision solutions

Machine vision algorithms

Deep learning algorithm is adapted to low power smart processor by low level matrix computation kernels, on chip memory flow management and native mixed precision floating point computation, which dramatically increase the unit performance.

3D Depth, Depth sensor in common use started by Kinect and today it could be small as notebook integrated camera (Intel have one). Applications are in augmented reality, 3D reconstruction and also detection. 

Tracking and Detection Object tracking and detection are great challenge in surveillance applications, indoor tracking and navigation and object detection. One of the interesting application is drones and autonomous robot navigation to catch target on cameras. 

Dev support

  • Set of optimized computer vision and math libs
  • C, C++ and OpenCL
  • Eclipse graphical development
  • Posix RTOS

Vision Processing Unit






Processor is based on Streaming Hybrid Architecture Vector Engines (SHAVE). This processor achieve the hundreds of GFLOPS in matrix multiplication in deep learning networks of various topologies.

There is some specifications of Myriad 2 SoC

  • Two trillion 16 bits operation with 500 mW of power usage
  • 12x 128-bit vector VLIW SHAVE type Processor for vision apps
  • 2x 32-bit RISC processor
  • 16 and 32 bit floating point operations
  • 8/16/32 bit integer operations
  • 2MB on chip memory

 

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