Object tracking and classification serve as basic components for the different perception tasks of autonomous robots. They provide the robot with the capability of class-aware tracking and richer features for decision-making processes. The joint estimation of class assignments, dynamic states and data associations results in a computationally intractable problem. Therefore, the vast majority of the literature tackles tracking and classification independently. The work presented here proposes a probabilistic model and an inference procedure that render the problem tractable through a structured variational approximation. The framework presented is very generic, and can be used for various tracking applications. It can handle objects with different dynamics, such as cars and pedestrians and it can seamlessly integrate multi-modal features, for example object dynamics and appearance. The method is evaluated and compared with state-of-the-art techniques using the publicly available KITTI dataset.
from robot theory http://ift.tt/1BW2OnT
