Human motion analysis with deep metric learning
Coskun H, Tan DJ, Conjeti S, Navab N, Tombari F (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Verlag
Book Volume: 11218 LNCS
Pages Range: 693-710
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Munich, DEU
ISBN: 9783030012632
DOI: 10.1007/978-3-030-01264-9_41
Abstract
Effectively measuring the similarity between two human motions is necessary for several computer vision tasks such as gait analysis, person identification and action retrieval. Nevertheless, we believe that traditional approaches such as L2 distance or Dynamic Time Warping based on hand-crafted local pose metrics fail to appropriately capture the semantic relationship across motions and, as such, are not suitable for being employed as metrics within these tasks. This work addresses this limitation by means of a triplet-based deep metric learning specifically tailored to deal with human motion data, in particular with the problem of varying input size and computationally expensive hard negative mining due to motion pair alignment. Specifically, we propose (1) a novel metric learning objective based on a triplet architecture and Maximum Mean Discrepancy; as well as, (2) a novel deep architecture based on attentive recurrent neural networks. One benefit of our objective function is that it enforces a better separation within the learned embedding space of the different motion categories by means of the associated distribution moments. At the same time, our attentive recurrent neural network allows processing varying input sizes to a fixed size of embedding while learning to focus on those motion parts that are semantically distinctive. Our experiments on two different datasets demonstrate significant improvements over conventional human motion metrics.
Involved external institutions
Germany (DE)How to cite
APA:
Coskun, H., Tan, D.J., Conjeti, S., Navab, N., & Tombari, F. (2018). Human motion analysis with deep metric learning. In Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss, Martial Hebert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 693-710). Munich, DEU: Springer Verlag.
MLA:
Coskun, Huseyin, et al. "Human motion analysis with deep metric learning." Proceedings of the 15th European Conference on Computer Vision, ECCV 2018, Munich, DEU Ed. Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss, Martial Hebert, Springer Verlag, 2018. 693-710.
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