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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
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Car NVIDIA deep Learning Platform. CES 2016

NVIDIA DRIVE PX2, deep learning for automotive 


NVIDIA Deep learning


Capabilities NVIDIA DRIVE™ PX 2

NVidia launched NVIDIA DRIVE™ PX 2 platform for in car AI deep learning. This processor is capable to  understand data from 4 x lidar detectors, 4 x fisheye cameras, 2 x narrow field cameras and GPS in real time. This NVIDIA's advanced GPUs are intended to handle 360 degree situations around the car from large amount of sensors data.
As official sources remark, The Drive PX2 provides super power equivalent to 150 MacBook Pros, which is really something impressive.

DRIVE™ PX 2 specifications

Platform is based on two next-generation Tegra processors and two special discrete GPU Pascal architecture, which is optimized for deep learning math acceleration up to 24 trillion operation over neural net per second. This power is capable to handle data from all the sensors at the same time in real time critical applications. Those 24 teraflops power can handle surround 365 view, pedestrian detection, smart collision control, traffic monitoring and driver state monitoring at the same time.

Brief about Pascal architecture


smart car Deep learning This GPU unit is capable to operate with half precision floating point format at 16-bit floating point accuracy at twice the rate than in case of 32-bit floating point accuracy. This is beneficial for the two key neural network parts, classification and convolution.   

3D memory is another key element of Pascal which provide 3x the bandwidth and the buffer capacity of previous Maxwell architecture. This feature is capable operate with deeper neural network. 

The last big think is NVLink which is communication bus that transfer date between GPU and CPU 5x up to 12x faster than PCI-Express. 


Read more Here Nvidia News





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Comments

  1. UnknownMarch 13, 2016 at 9:34 AM

    These type of car software and learning program really help the beginners. I read great reviews regarding these type of learning program.

    ReplyDelete
  2. UnknownJune 21, 2016 at 10:41 AM

    The software is useful. It will help people drive more safely. Thank you for sharing!

    ReplyDelete
  3. bashaMarch 23, 2019 at 4:47 AM

    Excellent blog I visit this blog it's really awesome. The important thing is that in this blog content written clearly and understandable. The content of information is very informative.
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