Abstract
The article presents a novel long-term object tracking method called SETh. It is an adaptive tracking by detection method which allows near real-time tracking within challenging sequences. The algorithm consists of three stages: detection, verification and learning. In order to measure the performance of the method a video data set consisting of more than a hundred videos was created and manually labelled by a human. Quality of the tracking by SETh was compared against five state-of-the-art methods. The presented method achieved results comparable and mostly exceeding the existing methods, which proves its capability for real life applications like e.g. vision-based control of UAVs.
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Jedrasiak, K., Andrzejczak, M., Nawrat, A. (2014). SETh: The Method for Long-Term Object Tracking. In: Chmielewski, L.J., Kozera, R., Shin, BS., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2014. Lecture Notes in Computer Science, vol 8671. Springer, Cham. https://doi.org/10.1007/978-3-319-11331-9_37
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DOI: https://doi.org/10.1007/978-3-319-11331-9_37
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