Computer Vision Informed Parameter Estimation

The proposed computer vision (CV) informed parameter estimation pipeline is capable of generating parameter estimates for a selected linear dynamic system, this is accomplished by (1) collecting a time-series dataset of a system’s physical motion, (2) relating the captured motion to a designed physics-based model, and (3) and estimating the most probable set of parameters that represents the observed motion using Bayesian inference. This analysis provides a fundamental understanding of the system’s intrinsic properties that enables the refinement and generation of new system affordances. Leveraging this additional knowledge, external learning systems may use this physics-based approach to make predictions of a system’s potential environmental interactions. We compare the results of our generalized approach with those of a handcrafted solution [1] designed for a similarly constructed experiment. Our experiments reveal a MAPE of 3.962% highlighting the approach’s robustness given a time-series dataset subject to additive-white Gaussian noise (AWGN). Github: https://tinyurl.com/CVIPE2025