Revisiting CNNs for Trajectory Similarity Learning

• URL: http://arxiv.org/abs/2405.19761v2
• Date: Tue, 05 Nov 2024 05:25:17 GMT
• Title: Revisiting CNNs for Trajectory Similarity Learning
• Authors: Zhihao Chang, Linzhu Yu, Huan Li, Sai Wu, Gang Chen, Dongxiang Zhang,
• Abstract summary: We introduce ConvTraj, incorporating both 1D and 2D convolutions to capture sequential and geo-distribution features of trajectories. We show that ConvTraj achieves state-of-the-art accuracy in trajectory similarity search.
• Score: 20.311950784166388
• License:
• Abstract: Similarity search is a fundamental but expensive operator in querying trajectory data, due to its quadratic complexity of distance computation. To mitigate the computational burden for long trajectories, neural networks have been widely employed for similarity learning and each trajectory is encoded as a high-dimensional vector for similarity search with linear complexity. Given the sequential nature of trajectory data, previous efforts have been primarily devoted to the utilization of RNNs or Transformers. In this paper, we argue that the common practice of treating trajectory as sequential data results in excessive attention to capturing long-term global dependency between two sequences. Instead, our investigation reveals the pivotal role of local similarity, prompting a revisit of simple CNNs for trajectory similarity learning. We introduce ConvTraj, incorporating both 1D and 2D convolutions to capture sequential and geo-distribution features of trajectories, respectively. In addition, we conduct a series of theoretical analyses to justify the effectiveness of ConvTraj. Experimental results on four real-world large-scale datasets demonstrate that ConvTraj achieves state-of-the-art accuracy in trajectory similarity search. Owing to the simple network structure of ConvTraj, the training and inference speed on the Porto dataset with 1.6 million trajectories are increased by at least $240$x and $2.16$x, respectively. The source code and dataset can be found at \textit{\url{https://github.com/Proudc/ConvTraj}}.

Related papers

• VeTraSS: Vehicle Trajectory Similarity Search Through Graph Modeling and Representation Learning [9.325787573209201]
  Trajectory similarity search plays an essential role in autonomous driving. We propose VeTraSS -- an end-to-end pipeline for Vehicle Trajectory Similarity Search.
  arXiv  Detail & Related papers  (2024-04-11T06:19:55Z)
• Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics [30.9735101687326]
  Trajectory Representation Learning (TRL) is a powerful tool for spatial-temporal data analysis and management. Existing TRL works usually treat trajectories as ordinary sequence data, while some important spatial-temporal characteristics, such as temporal regularities and travel semantics, are not fully exploited. We propose a novel Self-supervised trajectory representation learning framework with TemporAl Regularities and Travel semantics, namely START.
  arXiv  Detail & Related papers  (2022-11-17T13:14:47Z)
• Contrastive Trajectory Similarity Learning with Dual-Feature Attention [24.445998309807965]
  Tray similarity measures act as query predicates in trajectory databases. We propose a contrastive learning-based trajectory modelling method named TrajCL. TrajCL is consistently and significantly more accurate and faster than the state-of-the-art trajectory similarity measures.
  arXiv  Detail & Related papers  (2022-10-11T05:25:14Z)
• Large-Margin Representation Learning for Texture Classification [67.94823375350433]
  This paper presents a novel approach combining convolutional layers (CLs) and large-margin metric learning for training supervised models on small datasets for texture classification. The experimental results on texture and histopathologic image datasets have shown that the proposed approach achieves competitive accuracy with lower computational cost and faster convergence when compared to equivalent CNNs.
  arXiv  Detail & Related papers  (2022-06-17T04:07:45Z)
• Joint Spatial-Temporal and Appearance Modeling with Transformer for Multiple Object Tracking [59.79252390626194]
  We propose a novel solution named TransSTAM, which leverages Transformer to model both the appearance features of each object and the spatial-temporal relationships among objects. The proposed method is evaluated on multiple public benchmarks including MOT16, MOT17, and MOT20, and it achieves a clear performance improvement in both IDF1 and HOTA.
  arXiv  Detail & Related papers  (2022-05-31T01:19:18Z)
• PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map [58.53373202647576]
  We propose PreTraM, a self-supervised pre-training scheme for trajectory forecasting. It consists of two parts: 1) Trajectory-Map Contrastive Learning, where we project trajectories and maps to a shared embedding space with cross-modal contrastive learning, and 2) Map Contrastive Learning, where we enhance map representation with contrastive learning on large quantities of HD-maps. On top of popular baselines such as AgentFormer and Trajectron++, PreTraM boosts their performance by 5.5% and 6.9% relatively in FDE-10 on the challenging nuScenes dataset.
  arXiv  Detail & Related papers  (2022-04-21T23:01:21Z)
• Towards Similarity-Aware Time-Series Classification [51.2400839966489]
  We study time-series classification (TSC), a fundamental task of time-series data mining. We propose Similarity-Aware Time-Series Classification (SimTSC), a framework that models similarity information with graph neural networks (GNNs)
  arXiv  Detail & Related papers  (2022-01-05T02:14:57Z)
• Transformer Tracking [76.96796612225295]
  Correlation acts as a critical role in the tracking field, especially in popular Siamese-based trackers. This work presents a novel attention-based feature fusion network, which effectively combines the template and search region features solely using attention. Experiments show that our TransT achieves very promising results on six challenging datasets.
  arXiv  Detail & Related papers  (2021-03-29T09:06:55Z)
• An Unsupervised Learning Method with Convolutional Auto-Encoder for Vessel Trajectory Similarity Computation [13.003061329076775]
  We propose an unsupervised learning method which automatically extracts low-dimensional features through a convolutional auto-encoder (CAE) Based on the massive vessel trajectories collected, the CAE can learn the low-dimensional representations of informative trajectory images in an unsupervised manner. The proposed method largely outperforms traditional trajectory similarity methods in terms of efficiency and effectiveness.
  arXiv  Detail & Related papers  (2021-01-10T04:42:11Z)
• Comprehensive Graph-conditional Similarity Preserving Network for Unsupervised Cross-modal Hashing [97.44152794234405]
  Unsupervised cross-modal hashing (UCMH) has become a hot topic recently. In this paper, we devise a deep graph-neighbor coherence preserving network (DGCPN) DGCPN regulates comprehensive similarity preserving losses by exploiting three types of data similarities.
  arXiv  Detail & Related papers  (2020-12-25T07:40:59Z)
• Convolutional Embedding for Edit Distance [24.65097766064397]
  CNN-ED embeds edit distance into Euclidean distance for fast approximate similarity search. CNN-ED outperforms data-independent CGK embedding and RNN-based GRU embedding in terms of both accuracy and efficiency.
  arXiv  Detail & Related papers  (2020-01-31T07:53:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.