Real-Time and Approximate Iterative Optical Flow Implementation on Low-Power Embedded CPUs

Optical flow estimation is used in many embedded computer vision applications, and it is known to be computationally intensive. In the literature, many methods exist to estimate optical flow. Thus, the challenge is to find a method that matches the applicative constraints. In an embedded system, a trade-off between power consumption and execution time has to be made to meet both energy and framerate constraints. This work proposes methods to implement an approximate Horn & Schunck optical flow estimation that meets embedded CPUs constraints. This is achieved thanks to architectural optimizations, software optimizations and algorithm tuning. For instance, on the NVIDIA Jetson Nano, and for HD video sequences, the achieved frame latency is 12 ms for 5 Watts. To the best of our knowledge, this is the fastest optical flow implementation on embedded CPUs.