Abstract
Mice and rats can rapidly move their whiskers when exploring the environment. Accurate description of these movements is important for behavioral studies in neuroscience. Whisker tracking is, however, a notoriously difficult task due to the fast movements and frequent crossings and juxtapositionings among whiskers. We have recently developed WhiskEras, a computer-vision-based algorithm for whisker tracking in untrimmed, head-restrained mice. Although WhiskEras excels in tracking the movements of individual unmarked whiskers over time based on high-speed videos, the initial version of WhiskEras still had two issues preventing its widespread use: it involved tuning a great number of parameters manually to adjust for different experimental setups, and it was slow, processing less than 1 frame per second. To overcome these problems, we present here WhiskEras 2.0, in which the unwieldy stages of the initial algorithm were improved. The enhanced algorithm is more robust, not requiring intense parameter tuning. Furthermore, it was accelerated by first porting the code from MATLAB to C++ and then using advanced parallelization techniques with CUDA and OpenMP to achieve a speedup of at least 75x when processing a challenging whisker video. The improved WhiskEras 2.0 is made publicly available and is ready for processing high-speed videos, thus propelling behavioral research in neuroscience, in particular on sensorimotor integration.
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Arvanitis, P., Betting, JH.L.F., Bosman, L.W.J., Al-Ars, Z., Strydis, C. (2022). WhiskEras 2.0: Fast and Accurate Whisker Tracking in Rodents. In: Orailoglu, A., Jung, M., Reichenbach, M. (eds) Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2021. Lecture Notes in Computer Science, vol 13227. Springer, Cham. https://doi.org/10.1007/978-3-031-04580-6_14
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