Activity recognition by integrating the physics of motion with a Neuromorphic model of perception

In this paper, we propose a computational framework for integrating the physics of motion with the neurobiological basis of perception in order to model and recognize human actions and object activities. The essence, or gist, of an action is intrinsically related to the motion of the scene's objects. We define the Hamiltonian Energy Signature (HES) and derive the S-Metric to yield a global representation of the motion of the scene's objects in order to capture the gist of the activity. The HES is a scalar time-series that represents the motion of an object over the course of an activity and the S-Metric is a distance metric which characterizes the global motion of the object, or the entire scene, with a single, scalar value. The neurobiological aspect of activity recognition is handled by casting our analysis within a framework inspired by Neuromorphic Computing (NMC), in which we integrate a Motion Energy model with a Form/Shape model. We employ different Form/Shape representations depending on the video resolution but use our HES and S-Metric for the Motion Energy approach in either case. As the core of our Integration mechanism, we utilize variants of the latest neurobiological models of feature integration and biased competition, which we implement within a Multiple Hypothesis Testing (MHT) framework. Experimental validation of the theory is provided on standard datasets capturing a variety of problem settings: single agent actions (KTH), multi-agent actions, and aerial sequences (VIVID).