Abstract. Since the precise modeling of reflection is a difficult task, most feature points trackers assume that objects are lambertian and that no lighting change occurs. To some extent, a few approaches answer these issues by computing an affine photometric model or by achieving a photometric normalization. Through a study based on specular reflection models, we explain explicitly the assumptions on which these techniques are based. Then we propose a tracker that compensates for specular highlights and lighting variations more efficiently when small windows of interest are considered. Experimental results on image sequences prove the robustness and the accuracy of this technique in comparison with the existing trackers. Moreover, the computation time of the tracking is not significantly increased.
We present baseline results for a new task of automatic segmentation of Sign Language video into sentence-like units. We use a corpus of natural Sign Language video with accurately aligned subtitles to train a spatio-temporal graph convolutional network with a BiLSTM on 2D skeleton data to automatically detect the temporal boundaries of subtitles. In doing so, we segment Sign Language video into subtitle-units that can be translated into phrases in a written language. We achieve a ROC-AUC statistic of 0.87 at the frame level and 92% label accuracy within a time margin of 0.6s of the true labels.
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