With the advent of neuromorphic vision sensors such as event-based cameras, a paradigm shift is required for most computer vision algorithms. Among these algorithms, optical flow estimation is a prime candidate for this process considering that it is linked to a neuromorphic vision approach. Usage of optical flow is widespread in robotics applications due to its richness and accuracy. We present a Principal Component Analysis (PCA) approach to the problem of event-based optical flow estimation. In this approach, we examine different regularization methods which efficiently enhance the estimation of the optical flow. We show that the best variant of our proposed method, dedicated to the real-time context of visual odometry, is about two times faster compared to state-of-the-art implementations while significantly improves optical flow accuracy.
翻译:随着以事件为基础的照相机等神经形态视觉传感器的出现,大多数计算机视觉算法都需要进行范式转变。在这些算法中,光学流量估算是这一过程的首要选择,因为光学流量与神经形态的视觉方法有关。光学流量的使用由于其丰富性和准确性,在机器人应用中十分普遍。我们提出了一个主要组成部分分析(PCA)方法,以解决以事件为基础的光学流量估算问题。在这个方法中,我们研究了不同的正规化方法,这些方法有效地提高了光学流量的估计。我们表明,我们拟议方法中用于直观观察的实时环境的最佳变量比最新应用速度快两倍左右,同时大大提高光学流量的准确性。