The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new longterm tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website 60 .
Two-dimensional (2D) line scan-based dynamic magnetic resonance imaging (MRI) is examined as a means to capture the interior of objects under repetitive motion with high spatiotemporal resolutions. The method was demonstrated in a 9.4-T animal MRI scanner where line-by-line segmented k-space acquisition enabled recording movements of an agarose phantom and quail eggs in different conditions—raw and cooked. A custom MR-compatible actuator which utilized the Lorentz force on its wire loops in the scanner’s main magnetic field effectively induced the required periodic movements of the objects inside the magnet. The line-by-line k-space segmentation was achieved by acquiring a single k-space line for every frame in a motion period before acquisition of another line with a different phase-encode gradient in the succeeding motion period. The reconstructed time-course images accurately represented the objects’ displacements with temporal resolutions up to 5.5 ms. The proposed method can drastically increase the temporal resolution of MRI for imaging rapid periodic motion of objects while preserving adequate spatial resolution for internal details when their movements are driven by a reliable motion-inducing mechanism.
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