Towards Automated and Fast Whisker Tracking in Rodents

Abstract

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.