Abstract
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.
This work was supported by Fundação para a Ciência e a Tecnologia (ISR/IST plurianual funding) through the POS Conhecimento Program which includes FEDER funds.
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Nascimento, J.C., Figueiredo, M.A.T., Marques, J.S. (2009). Trajectory Modeling Using Mixtures of Vector Fields. In: Araujo, H., Mendonça, A.M., Pinho, A.J., Torres, M.I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2009. Lecture Notes in Computer Science, vol 5524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02172-5_7
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DOI: https://doi.org/10.1007/978-3-642-02172-5_7
Publisher Name: Springer, Berlin, Heidelberg
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