A variational approach to simultaneous tracking and classification of multiple objects

This paper presents a method for multi-object tracking which provides estimates of the dynamic state of the objects along with class identities. The estimated identities provide information about the objects´ behaviour, improving high level reasoning tasks. However, jointly estimating class assignments, dynamic states and data associations results in a computationally intractable problem. This paper proposes a probabilistic model for the multi-object tracking and classification problem, and an inference procedure that renders the problem tractable through a variational approximation. Our framework integrates the efficient Kalman filtering and smoothing recursions into a system that considers the dynamics of the environment to leverage both tracking and classification. The method is evaluated and compared to state-of-the-art techniques using stereo-vision data collected from a moving platform in urban scenarios.