Context-Associative Hierarchical Memory Model for Human Activity Recognition and Prediction

Human activity recognition is a challenging high-level vision task, for which multiple factors, such as subject, object, and their diverse interactions, have to be considered and modeled. Current learning-based methods are limited in the capability to integrate human-level concepts into an easily extensible computational framework. Inspired by the existing human memory model, we present a context-associative approach to recognize activity with human-object interaction. The proposed system can recognize incoming visual content based on the previous experienced activities. The high-level activity is parsed into consecutive subactivities, and we build a context cluster to model the temporal relations. The semantic attributes of the subactivity are organized by a concept hierarchy. Based on the hierarchy, a series of similarity functions are defined to turn the recognition computing into retrievals over the contextual memory, similar to the auto-associative characteristics of human memory. Partially matching in retrieval and stored memory make the activity prediction possible. The dynamical evolution of the brain memory is mimicked to allow decay and reinforcement of the input information, providing a natural way to maintain data and save computational time. We evaluate our approach on three data sets: CAD-120, MHOI, and OPPORTUNITY. The proposed method demonstrates promising results compared with other state-of-the-art techniques.