A survey on trajectory clustering analysis
arXiv:1802.06971
Abstract
This paper comprehensively surveys the development of trajectory clustering. Considering the critical role of trajectory data mining in modern intelligent systems for surveillance security, abnormal behavior detection, crowd behavior analysis, and traffic control, trajectory clustering has attracted growing attention. Existing trajectory clustering methods can be grouped into three categories: unsupervised, supervised and semi-supervised algorithms. In spite of achieving a certain level of development, trajectory clustering is limited in its success by complex conditions such as application scenarios and data dimensions. This paper provides a holistic understanding and deep insight into trajectory clustering, and presents a comprehensive analysis of representative methods and promising future directions.
References in corpus (4)
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Cited by in corpus (8)
- Deep Representation Learning and Clustering of Traffic Scenarios
- CoMoGCN: Coherent Motion Aware Trajectory Prediction with Graph Representation
- A Simplified Framework for Air Route Clustering Based on ADS-B Data
- Clustering in Recurrent Neural Networks for Micro-Segmentation using Spending Personality
- Informative Scene Decomposition for Crowd Analysis, Comparison and Simulation Guidance
- Context-free Self-Conditioned GAN for Trajectory Forecasting
- Evolutionary Clustering of Streaming Trajectories
- Capturing and Explaining Trajectory Singularities using Composite Signal Neural Networks