Hyper-Optimization tools comparison for parameter tuning applications
Camille Maurice, Francisco Madrigal, Frédéric Lerasle · 2017
This paper evaluates and compares different hyperparameters optimization tools that can be used in any vision applications for tuning their underlying free parameters. We focus in the problem of multiple object tracking, as it is widely studied in the literature and offers several parameters to tune. The selected tools are freely available or easy to implement. In this paper we evaluate the impact of parameter optimization tools over the tracking performances using videos from public datasets. Also, we discuss differences between the tools in term of performances, stability, documentation, etc.