Abstract
Visual tracking in low frame rate videos has many inherent difficulties for achieving accurate target recovery, such as occlusions, abrupt motions and rapid pose changes. Thus, conventional tracking methods cannot be applied reliably. In this paper, we offer a new scheme for tracking objects in low frame rate videos. We present a method of integrating multiple metrics for template matching, as an extension for the particle filter. By inspecting a large data set of videos for tracking, we show that our method not only outperforms other related benchmarks in the field, but it also achieves better results both visually and quantitatively, once compared to actual ground truth data.
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Liberman, Y., Perry, A. (2014). Visual Tracking Extensions for Accurate Target Recovery in Low Frame Rate Videos. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_13
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DOI: https://doi.org/10.1007/978-3-319-14249-4_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14248-7
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