Trajectory analysis plays a key role in human activity recognition and video surveillance. This paper proposes a new approach based on modeling trajectories using a bank of vector (velocity) fields. We assume that each trajectory is generated by one of a set of fields or by the concatenation of trajectories produced by different fields. The proposed approach constitutes a space-varying framework for trajectory modeling and is able to discriminate among different types of motion regimes. Furthermore, the vector fields can be efficiently learned from observed trajectories using an expectation-maximization algorithm. An experiment with real data illustrates the promising performance of the method.
['Jacinto C. Nascimento', 'Mário A. T. Figueiredo', 'Jorge S. Marques']
Trajectory Modeling Using Mixtures of Vector Fields