Thanks to the excellent learning capability of deep convolutional neural networks (CNNs), CNN-based methods have achieved great success in computer vision and image recognition tasks. However, it has turned out that these methods often have inherent vulnerabilities, which makes us cautious of the potential risks of using them for real-world applications such as autonomous driving. To reveal such vulnerabilities, we propose a method of simultaneously attacking monocular depth estimation and optical flow estimation, both of which are common artificial-intelligence-based tasks that are intensively investigated for autonomous driving scenarios. Our method can generate an adversarial patch that can fool CNN-based monocular depth estimation and optical flow estimation methods simultaneously by simply placing the patch in the input images. To the best of our knowledge, this is the first work to achieve simultaneous patch attacks on two or more CNNs developed for different tasks.

This paper presents a multi-resolution optical flow estimation method that is robust against large variation in the estimation parameter. For each level solution of the multi-grid estimation, a nonlinear iteration is proposed differently from the existing method, where the incremental displacement from the coarser level optical flow is calculated by linear iteration. The experimental results show that the proposed scheme has better error-performance in a much wider range of regularization parameters.

An optical flow-based motion estimation algorithm is proposed for video coding. The algorithm is based on the fact that true motion vectors have similar characteristics to optical flow vectors. The algorithm uses block matching motion estimation with an adaptive search region. The search region is computed from motion fields that are estimated based on the optical flow. The results obtained using test images show that the proposed algorithm can produce a significant improvement compared with previous optical flow algorithm and block matching algorithm.