Deep learning for location prediction on noisy trajectories

Precise tracking of a point-target on a nonlinear trajectory is challenging and has applications ranging from traffic analysis to microscopic particle tracking. To solve such a problem, we developed an algorithm which is independent of statistical-probabilistic and mechanical modeling, and free of a...

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Bibliographic Details
Published in:Pattern analysis and applications : PAA 2023-02, Vol.26 (1), p.107-122
Main Authors: Kandhare, Pravinkumar Gangadharrao, Nakhmani, Arie, Sirakov, Nikolay Metodiev
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Computer Science
Datasets
Deep learning
Machine learning
Noise sensitivity
Particle tracking
Pattern Recognition
Polar coordinates
Random noise
Statistical analysis
Theoretical Advances
Traffic analysis
Training
Trajectories
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Summary:Precise tracking of a point-target on a nonlinear trajectory is challenging and has applications ranging from traffic analysis to microscopic particle tracking. To solve such a problem, we developed an algorithm which is independent of statistical-probabilistic and mechanical modeling, and free of analytical extrapolation methods. Our main objective was, to predict target’s future location using its previous locations by a deep neural network, trained on a large data set of linear and nonlinear trajectories. To design our data-driven prediction approach, we developed a freely available database of up to second-order algebraic curves uniformly distributed in a given domain. This database could be used for training and testing point-target tracking algorithms. Simulated noisy test sets of trajectories were produced using Gaussian noise for analyzing the forecasting performance and noise sensitivity of our model. Further, the newly designed long short-term memory-based network that uses polar coordinates for its training is capable of predicting the target’s future locations on real-world smooth trajectories. We compared the proposed predictor network to classical and state-of-the-art predictors based on average absolute and relative errors. The experimental results demonstrated that our novel predictor achieved up to 47% improvement on test data sets. The observed area under the noise response curve has improved by up to 11%.
ISSN:1433-7541
1433-755X
DOI:10.1007/s10044-022-01095-y