Multiple Motion Fields for Multiple Types of Agents
Catarina Barata, Mário A. T. Figueiredo, Jorge Salvador Marques · 2019
Complex surveillance scenarios comprise different types of agents (e.g., bikers, cars, and pedestrians) that must be efficiently characterized in order to facilitate tasks such as tracking or abnormality detection. This paper proposes an unsupervised hierarchical multiple motion fields model to represent different types of agents, which relies in the combination of hierarchical Markov model and velocity fields. Model parameters are estimated using the expectation-maximization algorithm. The proposed framework was applied to synthetic and real datasets (Stanford Drone Dataset), showing the ability to characterize and classify different agents in an unsupervised way.