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.

Whisker tracking in rodents is an ongoing research in neuroscience. Neuroscientists have recorded experiments with high-speed cameras in which untrimmed, head-restrained mice are provided with air stimuli. These videos required the development of algorithms to reliably track whisker movement. Recently, a Whisker Tracking System, WhiskEras, emerged, which is able to detect and track whiskers over the course of such videos in a more accurate way than pre-existing methods. WhiskEras is slow, processing less than 1 frame per second. Additionally, it involves a great number of parameters which need tuning for different experimental setups and recording settings which were used for this experiment. This thesis addresses these two problems. First, the algorithm was examined and its shortcomings were exposed. A more accurate whisker point detection algorithm was suggested and implemented, among a range of alternative solutions which were studied. Furthermore, its Stitching stage was modified to replace a range of hard-to-tune parameters with more robust ones. Finally, the improved WhiskEras was accelerated by porting the MATLAB code to C++ and using advanced parallelization techniques with CUDA and OpenMP to achieve a speedup of 74.96. Overall, the improvements yielded better tracking results in our benchmarks, while the parameters were much easier to tune and remained constant under different video setups of whisking experiments.