  Data fusion has become an active research topic in recent years. Growing
computational performance has allowed the use of redundant sensors to measure a
single phenomenon. While Bayesian fusion approaches are common in general
applications, the computer vision field has largely relegated this approach.
Most object following algorithms have gone towards pure machine learning fusion
techniques that tend to lack flexibility. Consequently, a more general data
fusion scheme is needed. Within this work, a hierarchical Bayesian fusion
approach is proposed, which outperforms individual trackers by using redundant
measurements. The adaptive framework is achieved by relying on each
measurement's local statistics and a global softened majority voting. The
proposed approach was validated in a simulated application and two robotic
platforms.
