Improving a Switched Vector Field Model for Pedestrian Motion Analysis

Modeling the trajectories of pedestrians is a key task in video surveillance. However, finding a suitable model to describe the trajectories is challenging, mainly because several of the models tend to have a large number of parameters to be estimated. This paper addresses this issue and provides insights on how to tackle this problem. We model the trajectories using a mixture of vector fields with probabilistic switching mechanism that allows to efficiently change the trajectory motion. Depending on the probabilistic formulation, the motions fields can have a dense or sparse representation, which we believe influences the performance of the model. Moreover, the model has a large set of parameters that need to be estimated using the initialization-dependent EM-algorithm. To overcome the previous issues, an extensive study of the parameters estimation is conducted, namely: (i) initialization, and (ii) priors distribution that controls the sparsity of the solution. The various models are evaluated in the trajectory prediction task, using a newly proposed method. Experimental results in both synthetic and real examples provide new insights and valuable information how the parameters play an important in the proposed framework.