Siamese Multiple Attention Temporal Convolution Networks for Human Mobility Signature Identification

• URL: http://arxiv.org/abs/2408.09230v1
• Date: Sat, 17 Aug 2024 15:27:38 GMT
• Title: Siamese Multiple Attention Temporal Convolution Networks for Human Mobility Signature Identification
• Authors: Zhipeng Zheng, Yuchen Jiang, Shiyao Zhang, Xuetao Wei,
• Abstract summary: We propose a Siamese Multiple Attention Temporal Convolutional Network (Siamese MA-TCN) to capitalize on the strengths of both TCN architecture and multi-head self-attention. Experimental evaluations conducted on two real-world taxi trajectory datasets reveal that our proposed model effectively extracts both local key information and long-term dependencies.
• Score: 9.25278235266564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Human Mobility Signature Identification (HuMID) problem stands as a fundamental task within the realm of driving style representation, dedicated to discerning latent driving behaviors and preferences from diverse driver trajectories for driver identification. Its solutions hold significant implications across various domains (e.g., ride-hailing, insurance), wherein their application serves to safeguard users and mitigate potential fraudulent activities. Present HuMID solutions often exhibit limitations in adaptability when confronted with lengthy trajectories, consequently incurring substantial computational overhead. Furthermore, their inability to effectively extract crucial local information further impedes their performance. To address this problem, we propose a Siamese Multiple Attention Temporal Convolutional Network (Siamese MA-TCN) to capitalize on the strengths of both TCN architecture and multi-head self-attention, enabling the proficient extraction of both local and long-term dependencies. Additionally, we devise a novel attention mechanism tailored for the efficient aggregation of multi-scale representations derived from our model. Experimental evaluations conducted on two real-world taxi trajectory datasets reveal that our proposed model effectively extracts both local key information and long-term dependencies. These findings highlight the model's outstanding generalization capabilities, demonstrating its robustness and adaptability across datasets of varying sizes.

Related papers

• Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories [9.816270572121724]
  We propose Transferable Outlier Detection for Human Semantic Trajectories (TOD4Traj) framework. ToD4Traj first introduces a modality feature unification module to align diverse data feature representations. A contrastive learning module is further pro-posed for identifying regular mobility patterns both temporally and across populations.
  arXiv  Detail & Related papers  (2024-09-28T22:31:00Z)
• Self-Supervised State Space Model for Real-Time Traffic Accident Prediction Using eKAN Networks [18.385759762991896]
  SSL-eKamba is an efficient self-supervised framework for traffic accident prediction. To enhance generalization, we design two self-supervised auxiliary tasks that adaptively improve traffic pattern representation. Experiments on two real-world datasets demonstrate that SSL-eKamba consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-09-09T14:25:51Z)
• Unsupervised Representation Learning of Complex Time Series for Maneuverability State Identification in Smart Mobility [0.0]
  In smart mobility, MTS plays a crucial role in providing temporal dynamics of behaviors such as maneuver patterns. In this work, we aim to address challenges associated with modeling MTS data collected from a vehicle using sensors. Our goal is to investigate the effectiveness of two distinct unsupervised representation learning approaches in identifying maneuvering states in smart mobility.
  arXiv  Detail & Related papers  (2024-08-26T15:16:18Z)
• MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild [81.32127423981426]
  Multimodal emotion recognition based on audio and video data is important for real-world applications. Recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multimodal encoders. We propose a different perspective on the problem and investigate the advancement of multimodal DFER performance by adapting SSL-pre-trained disjoint unimodal encoders.
  arXiv  Detail & Related papers  (2024-04-13T13:39:26Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• Scalable Vehicle Re-Identification via Self-Supervision [66.2562538902156]
  Vehicle Re-Identification is one of the key elements in city-scale vehicle analytics systems. Many state-of-the-art solutions for vehicle re-id mostly focus on improving the accuracy on existing re-id benchmarks and often ignore computational complexity. We propose a simple yet effective hybrid solution empowered by self-supervised training which only uses a single network during inference time.
  arXiv  Detail & Related papers  (2022-05-16T12:14:42Z)
• Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification [7.5366501970852955]
  Multiple challenges hamper the applications of vision-based vehicle Re-ID methods. The proposed DRA model can automatically extract the discriminative region features, which can distinguish similar vehicles. And the OVG model can generate multi-view features based on the input view features to reduce the impact of viewpoint mismatches.
  arXiv  Detail & Related papers  (2022-04-28T07:46:03Z)
• Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation [102.24108167002252]
  We propose a novel attention network, named self-modulating attention, that models the complex and non-linearly evolving dynamic user preferences. We empirically demonstrate the effectiveness of our method on top-N sequential recommendation tasks, and the results on three large-scale real-world datasets show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-03-30T03:54:11Z)
• Diverse Complexity Measures for Dataset Curation in Self-driving [80.55417232642124]
  We propose a new data selection method that exploits a diverse set of criteria that quantize interestingness of traffic scenes. Our experiments show that the proposed curation pipeline is able to select datasets that lead to better generalization and higher performance.
  arXiv  Detail & Related papers  (2021-01-16T23:45:02Z)
• AttnMove: History Enhanced Trajectory Recovery via Attentional Network [15.685998183691655]
  We propose a novel attentional neural network-based model, named AttnMove, to densify individual trajectories by recovering unobserved locations. We evaluate our model on two real-world datasets, and extensive results demonstrate the performance gain compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-01-03T15:45:35Z)
• A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction [74.00750936752418]
  We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC) First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
  arXiv  Detail & Related papers  (2020-03-13T04:35:50Z)

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.