Ing. Jonáš Šerých

Optical flow is a useful input for various applications, including 3D reconstruction, pose estimation, tracking, and structure-from-motion. Despite its utility, the field of dense long-term tracking, especially over wide baselines, has not been extensively explored. This paper extends the concept of combining multiple optical flows over logarithmically spaced intervals as proposed by MFT. We demonstrate the compatibility of MFT with different optical flow networks, yielding results that surpass their individual performance. Moreover, we present a simple yet effective combination of these networks within the MFT framework. This approach proves to be competitive with more sophisticated, non-causal methods in terms of position prediction accuracy, highlighting the potential of MFT in enhancing long-term tracking applications.

We introduce a new video analysis problem – tracking of rigid planar objects in sequences where both their sides are visible. Such coin-like objects often rotate fast with respect to an arbitrary axis producing unique challenges, such as fast incident light and aspect ratio change and rotational motion blur. Despite being common, neither tracking sequences containing coin-like objects nor suitable algorithm have been published. As a second contribution, we present a novel coin-tracking benchmark containing 17 video sequences annotated with object segmentation masks. Experiments show that the sequences differ significantly from the ones encountered in standard tracking datasets. We propose a baseline coin-tracking method based on convolutional neural network segmentation and explicit pose modeling. Its performance confirms that coin-tracking is an open and challenging problem.

We revisit the problem of local optimization (LO) in RANSAC for homography estimation. The standard state-of-the-art LO-RANSAC improves the plain version's accuracy and stability, but it may be computationally demanding, it is complex to implement and requires setting multiple parameters. We show that employing L1 minimization instead of the standard LO step of LO-RANSAC leads to results with similar precision. At the same time, the proposed L1 minimization is significantly faster than the standard LO step of [8], it is easy to implement and it has only a few of parameters which all have intuitive interpretation. On the negative side, the L1 minimization does not achieve the robustness of the standard LO step, its probability of failure is higher.