The ability to detect potentially interacting agents in the surrounding environment is acknowledged to be one of the first perceptual tasks developed by humans, supported by the ability to recognise biological motion. The precocity of this ability suggests that it might be based on rather simple motion properties, and it can be interpreted as an atomic building block of more complex perception tasks typical of interacting scenarios, as the understanding of non-verbal communication cues based on motion or the anticipation of others' action goals. In this article, we propose a novel perspective for video analysis, bridging cognitive science and machine vision, which leverages the use of computational models of the perceptual primitives that are at the basis of biological motion perception in humans. Our work offers different contributions. In a first part, we propose an empirical formulation for the Two-Thirds Power Law, a well-known invariant law of human movement, and thoroughly discuss its readability in experimental settings of increasing complexity. In particular, we consider unconstrained video analysis scenarios, where, to the best of our knowledge, the invariant law has not found application so far. The achievements of this analysis pave the way for the second part of the work, in which we propose and evaluate a general representation scheme for biological motion characterisation to discriminate biological movements with respect to non-biological dynamic events in video sequences. The method is proposed as the first layer of a more complex architecture for behaviour analysis and human-machine interaction, providing in particular a new way to approach the problem of human action understanding.

Exploring biological motion regularities of human actions: A new perspective on video analysis

Noceti, Nicoletta;Odone, Francesca;Sandini, Giulio
2017

Abstract

The ability to detect potentially interacting agents in the surrounding environment is acknowledged to be one of the first perceptual tasks developed by humans, supported by the ability to recognise biological motion. The precocity of this ability suggests that it might be based on rather simple motion properties, and it can be interpreted as an atomic building block of more complex perception tasks typical of interacting scenarios, as the understanding of non-verbal communication cues based on motion or the anticipation of others' action goals. In this article, we propose a novel perspective for video analysis, bridging cognitive science and machine vision, which leverages the use of computational models of the perceptual primitives that are at the basis of biological motion perception in humans. Our work offers different contributions. In a first part, we propose an empirical formulation for the Two-Thirds Power Law, a well-known invariant law of human movement, and thoroughly discuss its readability in experimental settings of increasing complexity. In particular, we consider unconstrained video analysis scenarios, where, to the best of our knowledge, the invariant law has not found application so far. The achievements of this analysis pave the way for the second part of the work, in which we propose and evaluate a general representation scheme for biological motion characterisation to discriminate biological movements with respect to non-biological dynamic events in video sequences. The method is proposed as the first layer of a more complex architecture for behaviour analysis and human-machine interaction, providing in particular a new way to approach the problem of human action understanding.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/886487
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