Evaluation of Approaches for Action Similarity Measurement - Luo, Guoliang
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Programming by Demonstration has been recently proposed as a way for a robot learning tasks from human demonstrations, where action recognition is a crucial step in the procedure. Based on this concept, a model-free approach for object manipulation was proposed by Aksoy et al.[1]. In specific, the approach classifies actions by observing object-interaction changes based on video segmentation. However, the segmentation suffers from various difficulties, such as motion blur, complex environment, over- and under- segmentation. For this reason, we simulate and evaluate the Aksoy et al.'s method.…mehr

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Produktbeschreibung
Programming by Demonstration has been recently proposed as a way for a robot learning tasks from human demonstrations, where action recognition is a crucial step in the procedure. Based on this concept, a model-free approach for object manipulation was proposed by Aksoy et al.[1]. In specific, the approach classifies actions by observing object-interaction changes based on video segmentation. However, the segmentation suffers from various difficulties, such as motion blur, complex environment, over- and under- segmentation. For this reason, we simulate and evaluate the Aksoy et al.'s method. Additionally, we adapt a kernel based representation into Aksoy et al.'s method. The experiments shows the new method improves action recognition rate significantly.
Autorenporträt
Guoliang Luo earned his PhD degree from University of Strasbourg, France, in 2014. Prior to this, he obtained his master degree in computer science from Uppsala University, Sweden. His current research interests include computer vision and computer graphics, the segmentation techniques of videos and animated meshes, and their applications.