Intelligent Sparse Visual Motion Estimation Method Using Local Activation Model for Multi-Aperture Imaging Systems

Adaptive visual motion perception has major engineering significance in bionic vision for internet of things (IoT) applications, particularly for unmanned motion platforms. However, the most advanced multi-aperture array imaging systems (MAS), calculating global dense optical flow is often computationally prohibitive and conflicts with the motionsensitive characteristics of biomimetic compound-eye(BCE). Here, we report a motion estimation method that joint sparse optical flow estimation by selectively activating aperture motion flow to minimize redundant calculations. Furthermore, to solve the aperture correlation matching challenge, a scale-invariant feature transform(SIFT) acceleration network is proposed based on the customized aperture descriptors, employing a multi-layer perceptron (MLP) for feature separation. Finally, within the framework of graph signal processing (GSP) and sparse coding, our results validate the feasibility of the proposed strategy, offering a promising solution for visual motion measurement in intelligent bionic vision systems.