A class of space-varying parametric motion fields for human activity recognition

Video cameras monitoring human activities in public spaces are commonplace in cities worldwide. Such monitoring task is important for safety and security purposes but is also extremely challenging. In this paper, we propose a class of algorithms for far-field human activity recognition, a central task in video surveillance. More specifically, we explore a class of parametric motion vector fields learned from the trajectories described by people in real-world scenarios. The work proposed herein is a space dependent framework, in sense that the vector fields depend on the pedestrian position. Thus, the model is flexible leading to an expressive description of complex trajectories. Also, a model selection strategy is addressed to automatically choose the appropriate number of underlying motion fields presented in the trajectories. Experimental evaluation is conducted in real settings testifying the usefulness of the proposed approach for human activity recognition.