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Shape-aware spatio-temporal descriptors for interaction classification

Abstract : Many real-world tasks for autonomous agents benefit from understanding dynamic inter-object interactions. Detecting, analyzing and differentiating between the various ways that an object can be interacted with provides implicit information about its function. This can help train autonomous agents to handle objects and understand unknown scenes. We describe a general mathematical framework to analyze and classify interactions, defined as dynamic motions performed by an active object onto a passive one. We factorize interactions via motion features computed in the spatio-temporal domain, and encoded into a global, object-centric signature. Equipped with a similarity measure to compare such signatures, we showcase classification of interactions with a single object. We also propose a novel acquisition setup combining RGBD sensing with a virtual reality (VR) display, to capture interactions with purely virtual objects.
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Contributor : Boris Thibert <>
Submitted on : Monday, October 1, 2018 - 9:49:07 AM
Last modification on : Tuesday, May 11, 2021 - 11:37:30 AM




Soren Pirk, Olga Diamanti, Boris Thibert, Danfei Xu, Leonidas Guibas. Shape-aware spatio-temporal descriptors for interaction classification. 2017 IEEE International Conference on Image Processing (ICIP), Sep 2017, Beijing, France. ⟨10.1109/ICIP.2017.8297139⟩. ⟨hal-01884481⟩



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