A Spatio-Temporal Feature Trajectory Clustering Algorithm Based on Deep Learning
Abstract
:1. Introduction
2. Problem Analysis
3. A Spatio-Temporal Feature Trajectory Clustering Algorithm
3.1. Image-Based Trajectory Spatial Shape Feature Extraction Algorithm
3.1.1. Trajectory Imaging
3.1.2. SURF Similarity Matching
3.1.3. Feature Dimensionality Reduction
3.2. Spatio-Temporal Feature Trajectory Clustering Algorithm
3.2.1. Temporal Feature Extraction
3.2.2. Feature Fusion
3.2.3. Clustering
4. Experiment and Analysis
4.1. Experimental Design
4.1.1. Performance Index
4.1.2. Data Sources
4.2. Verification of Trajectory Spatial Shape Feature Extraction
4.2.1. Simulation Datasets
4.2.2. SURF Algorithm Effect Verification
4.2.3. Verification of Spatial Shape Feature Extraction Process
4.2.4. Verification of Feature Fusion Effect
4.3. Algorithm Comparison
4.3.1. Datasets
4.3.2. Experimental Results
5. Conclusions
- (1)
- An image-based trajectory spatial shape feature extraction algorithm is proposed. It is used to extract the overall shape features of the trajectory and is robust to changes such as rotation and scaling of the trajectory.
- (2)
- The extracted spatial shape features and temporal features are fused, and a trajectory clustering method based on the fusion of temporal and spatial features is proposed to get better clustering performance.
- (3)
- The performance of the algorithm is verified by experiments on simulated datasets and actual ADS-B and GPS datasets. The experimental results show that the algorithm in this paper can effectively extract the trajectory spatial shape features and obtain better clustering performance.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Symbol | Description |
---|---|
the i-th trajectory in the trajectory dataset | |
the k-th point in the i-th trajectory | |
the number of trajectory feature points | |
the number of match feature points | |
the similarity of trajectories calculated by SURF method | |
the distance between the i-th and the j-th trajectory | |
the number of track categories | |
the number of matching samples trajectories | |
the trajectory shape feature vector | |
the final generated feature vector |
Clustering Features | Purity | KL Divergence |
---|---|---|
Temporal Features | 0.73 | 0.22 |
Spatial Shape Features | 0.87 | 0.05 |
Feature Stitching | 0.89 | 0.06 |
Feature Fusion | 0.93 | 0.06 |
Data | Clustering Algorithm | Purity | KL Divergence |
---|---|---|---|
ADS-B | SURF | 0.729 | 0.160 |
Temporal Autoencoder | 0.718 | 0.168 | |
Our Algorithm | 0.882 | 0.044 | |
DTW | 0.702 | 0.219 | |
MFA Autoencoder | 0.790 | 0.154 | |
GeoLife | SURF | 0.719 | 0.129 |
Temporal Autoencoder | 0.744 | 0.149 | |
Our Algorithm | 0.884 | 0.052 | |
DTW | 0.791 | 0.158 | |
MFA Autoencoder | 0.836 | 0.068 |
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He, X.; Li, Q.; Wang, R.; Chen, K. A Spatio-Temporal Feature Trajectory Clustering Algorithm Based on Deep Learning. Electronics 2022, 11, 2283. https://doi.org/10.3390/electronics11152283
He X, Li Q, Wang R, Chen K. A Spatio-Temporal Feature Trajectory Clustering Algorithm Based on Deep Learning. Electronics. 2022; 11(15):2283. https://doi.org/10.3390/electronics11152283
Chicago/Turabian StyleHe, Xintai, Qing Li, Runze Wang, and Kun Chen. 2022. "A Spatio-Temporal Feature Trajectory Clustering Algorithm Based on Deep Learning" Electronics 11, no. 15: 2283. https://doi.org/10.3390/electronics11152283
APA StyleHe, X., Li, Q., Wang, R., & Chen, K. (2022). A Spatio-Temporal Feature Trajectory Clustering Algorithm Based on Deep Learning. Electronics, 11(15), 2283. https://doi.org/10.3390/electronics11152283