GeoPTH: A Lightweight Approach to Category-Based Trajectory Retrieval via Geometric Prototype Trajectory Hashing

TL;DR

GeoPTH introduces a lightweight, non-learning trajectory retrieval method that uses geometric prototypes for efficient and accurate category-based search, outperforming existing methods in speed and competitiveness.

Contribution

The paper presents GeoPTH, a novel, non-learning framework that constructs data-dependent hash functions using geometric prototypes for efficient trajectory retrieval.

Findings

GeoPTH achieves high retrieval accuracy comparable to state-of-the-art methods.

GeoPTH significantly outperforms simple binarization of learned embeddings.

GeoPTH demonstrates superior efficiency in trajectory retrieval tasks.

Abstract

Trajectory similarity retrieval is an important part of spatiotemporal data mining, however, existing methods have the following limitations: traditional metrics are computationally expensive, while learning-based methods suffer from substantial training costs and potential instability. This paper addresses these problems by proposing Geometric Prototype Trajectory Hashing (GeoPTH), a novel, lightweight, and non-learning framework for efficient category-based trajectory retrieval. GeoPTH constructs data-dependent hash functions by using representative trajectory prototypes, i.e., small point sets preserving geometric characteristics, as anchors. The hashing process is efficient, which involves mapping a new trajectory to its closest prototype via a robust, Hausdorff metric. Extensive experiments show that GeoPTH's retrieval accuracy is highly competitive with both traditional metrics and state-of-the-art learning methods, and it significantly outperforms binary codes generated through simple binarization of the learned embeddings. Critically, GeoPTH consistently outperforms all competitors in terms of efficiency. Our work demonstrates that a lightweight, prototype-centric approach offers a practical and powerful alternative, achieving an exceptional retrieval performance and computational efficiency.