A sparse approach to pedestrian trajectory modeling using multiple motion fields

This paper proposes a novel methodology to describe the trajectories performed by pedestrians in long-range surveillance scenarios. The proposed approach describes the trajectories by using sparse motion/vector fields together with a space-varying switching mechanism embedded in a Hidden Markov Model framework. Despite the diversity of motion patterns that may occur in a given scenario, the observed trajectories do not lie in the entire surveilled area. Instead, they are constrained to patterns corresponding to typical motions. To achieve a compact representation, we propose a sparse model estimated using the ℓ1 norm applied to the log prior distribution of the vector fields. Experimental evaluation is conducted in real scenarios, and testify the usefulness of the proposed approach in modeling typical trajectories that occur in a far-field surveillance setup.