GPI Seminar Series: Adolfo López

Adolfo López,  Metric Learning from Poses for Temporal Clustering of Human Motion
Wednesday September 26th, at 15:00, Seminar Room D5-007

In this talk, we present an approach towards temporal clustering of human behavior in motion sequences. Temporal clustering of human motion into semantically meaningful behaviors is a challenging task. While unsupervised methods do well to some extent, the obtained clusters often lack a semantic interpretation. We propose to learn what makes a sequence of human poses different from others such that it should be annotated as an action. To this end, we formulate the problem as a weakly supervised temporal clustering for an unknown number of clusters. Weak supervision is attained by learning a metric from the implicit semantic distances derived from already annotated databases. Such a metric contains some low-level semantic information that can be used to effectively segment a human motion sequence into distinct actions or behaviors. The main advantage of our approach is that metrics can be successfully used across datasets, making our method a compelling alternative to unsupervised methods. Experiments on publicly available mocap datasets show the effectiveness of our approach. This work was developed during a 3 month stay at ETH Zurich, in collaboration with doctor Juergen Gall, and it has been recently presented at BMVC 2012.