Deep Siamese Metric Learning: A Highly Scalable Approach to Searching Unordered Sets of Trajectories

Löffler C, Reeb L, Dzibela D, Marzilger R, Witt N, Eskofier B, Mutschler C (2022)

Publication Type: Journal article

Publication year: 2022

Journal

ACM Transactions on Intelligent Systems and Technology Association for Computing Machinery (ACM)

Book Volume: 13

Article Number: 6

Journal Issue: 1

DOI: 10.1145/3465057

Abstract

This work proposes metric learning for fast similarity-based scene retrieval of unstructured ensembles of trajectory data from large databases. We present a novel representation learning approach using Siamese Metric Learning that approximates a distance preserving low-dimensional representation and that learns to estimate reasonable solutions to the assignment problem. To this end, we employ a Temporal Convolutional Network architecture that we extend with a gating mechanism to enable learning from sparse data, leading to solutions to the assignment problem exhibiting varying degrees of sparsity.Our experimental results on professional soccer tracking data provides insights on learned features and embeddings, as well as on generalization, sensitivity, and network architectural considerations. Our low approximation errors for learned representations and the interactive performance with retrieval times several magnitudes smaller shows that we outperform previous state of the art.

Authors with CRIS profile

Christoffer Löffler Machine Learning and Data Analytics Lab Luca Reeb Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) (LME) Björn Eskofier Machine Learning and Data Analytics Lab

Involved external institutions

Fraunhofer-Institut für Integrierte Systeme und Bauelementetechnologie (IISB) DE Germany (DE)

How to cite

APA:

Löffler, C., Reeb, L., Dzibela, D., Marzilger, R., Witt, N., Eskofier, B., & Mutschler, C. (2022). Deep Siamese Metric Learning: A Highly Scalable Approach to Searching Unordered Sets of Trajectories. ACM Transactions on Intelligent Systems and Technology, 13(1). https://doi.org/10.1145/3465057

MLA:

Löffler, Christoffer, et al. "Deep Siamese Metric Learning: A Highly Scalable Approach to Searching Unordered Sets of Trajectories." ACM Transactions on Intelligent Systems and Technology 13.1 (2022).

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