<p>For dense optical flow estimation in real-time setup, <a href="https://arxiv.org/pdf/1612.01925.pdf" rel="nofollow noreferrer">FlowNet</a> is a good option. It can achieve optical flow estimation at a higher FPS. You can take their trained model to perform inference. Since you want to run the estimation in a non-GPU environment, you can try converting the model to <a href="https://github.com/onnx/onnx/blob/master/docs/Overview.md" rel="nofollow noreferrer">ONNX</a> format. A good implementation of FlowNet is available in NVIDIA's Github <a href="https://github.com/NVIDIA/flownet2-pytorch" rel="nofollow noreferrer">repo</a>. I am not sure exactly which algorithm NVIDIA is using in its SDK for optical flow.</p>
<p>The FlowNet2 is built upon previous work of FlowNet to compute large displacement. However, if you are concerned about occlusion then you may check out their follow up work on FlowNet3. Another alternative to FlowNet is <a href="https://arxiv.org/pdf/1709.02371.pdf" rel="nofollow noreferrer">PwC-Net</a>.</p>
Dense optical flow for real time on hardware for 4K resolution @30fps
<p>I am working on a hardware-based solution ( without GPU) for dense optical flow to get real-time performance @ 30fps with decent accuracy. Something comparable to or better than NVIDIA’s optical flow SDK. Can someone please suggest good algorithms other than Pyramidal Lukas Kanade and horn Schunck. I found SGM as a good starting point but it’s difficult to implement on FPGA or DSP core. The target is to measure large displacements with occlusion as well as similar to real-world videos.</p>
<p>It would be great if someone could tell what exactly algorithm NVIDIA has used.</p>