Anatomical-plane-based representation for human–human interactions analysis. Issue 8 (August 2015) - Alazrai, Rami; Mowafi, Yaser; George Lee, C.S.
Abstract In this paper, we present a novel view-invariant, motion-pose geometric descriptor (MPGD) as a human–human interaction representation to capture the semantic meaning of body-parts between two interacting humans. The proposed MPGD representation is based on utilizing the concept of anatomical planes to construct a motion profile and a pose profile for each human. Those two profiles are then concatenated to form a descriptor for the two interacting humans. Using the proposed MPGD representation, we study two problems related to human–human interaction analysis, namely human–human interaction classification and prediction. For the human–human interaction classification problem, we propose a hierarchical classification framework consisting of a representation layer and three classification layers. The classification framework aims to realize what is the performed interaction in an input video by understanding how and when each individual performed sub-activities to each other over time. The human–human interaction prediction problem aims to predict the class of ongoing human–human interaction at its early stages. To do so, we propose a prediction framework that utilizes the proposed MPGD to construct an accumulated histograms-based representation for an ongoing interaction. The accumulated histograms of MPGDs are then used to train a set of support-vector-machine classifiers with a probabilistic output to predict the class of an ongoing interaction. In order to evaluate our proposed MPGD representation and both the classification and the prediction frameworks, we utilize a Microsoft Kinect sensor to capture human–human interactions in a video dataset that consists of 12 interactions performed by 12 individuals. We evaluate the performance of our proposed classification framework and compare the results with an appearance-based representation and a representation that combines both the MPGD representation and the appearance-based representation. On the one hand, our proposed MPGD representation performance has shown promising results compared to the appearance-based representation with an average accuracy of 94.86% in classifying human–human interactions. On the other hand, human–human interaction prediction framework has achieved an average prediction accuracy of 82.46% with only 50% of the interaction video being observed.