Detection of dog-robot interactions in video sequences

Abstract

This paper propose a novel framework for unsupervised detection of object interactions in video sequences based on dynamic features. The goal of our system is to process videos in an unsupervised manner using Hierarchical Bayesian Topic Models, specifically the Hierarchical Dirichlet Processes (HDP). We investigate how low-level features such as optical flow combined with Hierarchical Dirichlet Process (HDP) can help to recognize meaningful interactions between objects in the scene. For example, in videos of animal interaction recordings, kicking ball, standing, moving around etc. The underlying hypothesis that to validate is that interactions in such scenarios are heavily characterized by their 2D spatio-temporal features. Various experiments have been performed on the challenging JAR-AIBO dataset and first promising results are reported.