LAUSR

Smart homes are becoming a growing need to prepare for a comfortable life style for the elderly and make things easy for the caretakers of the future. One important component of these systems is to identify the human activities and scenarios. As the wireless technologies are becoming advanced, they are being used to provide a low-cost, non-intrusive and privacy-conscious solution to activity recognition. However, in more complicated environments, we need to identify scenarios with subtle cues e.g. eye gaze. These situations call for a complementary vision-based solution, and we present a robust scenario recognition system by following the objects seen in eye gaze trajectories. In this paper, we present a probabilistic hierarchical model for scenario recognition using the environmental elements like objects in the scene. We utilize the fact that any scenario can be divided into constituent tasks and activities recursively to the level of atomic actions and objects. Considering bottom-up, the scenario recognition problem can be hierarchically solved by identifying the objects seen and combining them together to form coarse-grained higher level activities. This is a novel contribution to be able to recognize complete scenarios only on the basis of objects seen. We performed experiments on standard datasets of Georgia Tech Egocentric Activities (GTEA-Gaze) and unconstrained videos collected “in the Wild”; and trained an Artificial Neural Network to get a precision of 73.84% and accuracy of 92.27%.