In this paper, we demonstrate how the differential Earth Mover's Distance (EMD) may be used for visual tracking in synergy with Gaussian mixtures models (GMM). According to our model, motion between adjacent frames results in variations of the mixing proportions of the Gaussian components representing the object to be tracked. These variations are computed in closed form by minimizing the differential EMD between Gaussian mixtures, yielding a very fast algorithm with high accuracy, without recurring to the EM algorithm in each frame. Moreover, we also propose a framework to handle occlusions, where the prediction for the object's location is forwarded to an adaptive Kalman filter whose parameters are estimated on line by the motion model already observed. Experimental results show significant improvement in tracking performance in the presence of occlusion.

A learning-based framework for action representation and recognition relying on the description of an action by time series of optical flow motion features is presented. In the learning step, the motion curves representing each action are clustered using Gaussian mixture modeling (GMM). In the recognition step, the optical flow curves of a probe sequence are also clustered using a GMM, then each probe sequence is projected onto the training space and the probe curves are matched to the learned curves using a non-metric similarity function based on the longest common subsequence, which is robust to noise and provides an intuitive notion of similarity between curves. Alignment between the mean curves is performed using canonical time warping. Finally, the probe sequence is categorized to the learned action with the maximum similarity using a nearest neighbor classification scheme. We also present a variant of the method where the length of the time series is reduced by dimensionality reduction in both training and test phases, in order to smooth out the outliers, which are common in these type of sequences. Experimental results on KTH, UCF Sports and UCF YouTube action databases demonstrate the effectiveness of the proposed method.

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A serosurvey of IgG antibodies against SARS-CoV-2 was conducted in Greece between May and August 2020. It was designed as a cross-sectional survey and was repeated at monthly intervals. The leftover sampling methodology was used and a geographically stratified sampling plan was applied. Of 20,110 serum samples collected, 89 (0.44%) were found to be positive for anti-SARS-CoV-2 antibodies, with higher seroprevalence (0.35%) observed in May 2020. The highest seroprevalence was primarily observed in the “30–49” year age group. Females presented higher seroprevalence compared to males in May 2020 (females: 0.58% VS males: 0.10%). This difference reversed during the study period and males presented a higher proportion in August 2020 (females: 0.12% VS males: 0.58%). Differences in the rate of seropositivity between urban areas and the rest of the country were also observed during the study period. The four-month infection fatality rate (IFR) was estimated to be 0.47%, while the respective case fatality rate (CFR) was at 1.89%. Our findings confirm low seroprevalence of COVID-19 in Greece during the study period. The young adults are presented as the most affected age group. The loss of the cumulative effect of seropositivity in a proportion of previous SARS-CoV-2 infections was indicated.