Maximum Likelihood Multiple Model Filtering for Path Prediction in Intelligent Transportation Systems

Vashishtha, Deexa; Panda, Manoj Kumar

doi:10.1016/j.procs.2018.10.453

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…Shao X et al [ 15 ] presented a unique filtering technique, which is to follow a target’s mobility utilizing GPS sensors. Vashishtha D et al [ 16 ] and Kapania S et al [ 17 ] improved particle filtering, combined color sequences, and constrained Bayesian state estimation to achieve motion trajectory prediction of the target. Choi D et al [ 18 ] proposed a method using maximum likelihood multi-filter to obtain an overall estimate to predict the target trajectory by combining independent multiple kinematic model correlation estimates through a great likelihood rule.…”

Section: Related Workmentioning

confidence: 99%

Video Abnormal Behavior Recognition and Trajectory Prediction Based on Lightweight Skeleton Feature Extraction

Wang,

Ding,

Zhang

et al. 2024

Sensors

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Video action recognition based on skeleton nodes is a highlighted issue in the computer vision field. In real application scenarios, the large number of skeleton nodes and behavior occlusion problems between individuals seriously affect recognition speed and accuracy. Therefore, we proposed a lightweight multi-stream feature cross-fusion (L-MSFCF) model to recognize abnormal behaviors such as fighting, vicious kicking, climbing over the wall, et al., which could obviously improve recognition speed based on lightweight skeleton node calculation, and improve recognition accuracy based on occluded skeleton node prediction analysis in order to effectively solve the behavior occlusion problem. The experiments show that our proposed All-MSFCF model has a video action recognition average accuracy rate of 92.7% for eight kinds of abnormal behavior recognition. Although our proposed lightweight L-MSFCF model has an 87.3% average accuracy rate, its average recognition speed is 62.7% higher than the full-skeleton recognition model, which is more suitable for solving real-time tracing problems. Moreover, our proposed Trajectory Prediction Tracking (TPT) model could real-time predict the moving positions based on the dynamically selected core skeleton node calculation, especially for the short-term prediction within 15 frames and 30 frames that have lower average loss errors.

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Section: Related Workmentioning

confidence: 99%

Video Abnormal Behavior Recognition and Trajectory Prediction Based on Lightweight Skeleton Feature Extraction

Wang,

Ding,

Zhang

et al. 2024

Sensors

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show abstract

“…The Kalman filter (KF) uses new observation data to predict the trajectory of the next moment and is used to predict latitude and longitude [26][27][28]. The Kalman filter is a type of prediction method with high precision.…”

Section: Probabilistic Statistical Modelmentioning

confidence: 99%

Prediction techniques of spatiotemporal trajectory data: progress and framework

Qin,

Ran,

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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With the wide application of intelligent devices, a large amount of spatiotemporal trajectory data can be preserved and recorded. The application of spatiotemporal trajectories has penetrated every aspect of people's daily lives. It is of great applications value to predict the future position of a moving object by mining its motion law. Data are the basis of the method, and thus, the form, source and characteristics of spatiotemporal trajectory data are discussed in this paper. Second, trajectory prediction technology is introduced, and its advantages, disadvantages and applications scope are summarized. Trajectory prediction technology can be divided into trajectory prediction methods based on constructing motion models, pattern mining and deep learning. Finally, the problems and potential research directions of trajectory prediction technology are discussed.

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“…Unscented Kalman Filter (UKF) can avoid the complex operation of the complex nonlinear function Jacobian matrix; to ensure the universal adaptability of the nonlinear system. Document [7] trackless Kalman filter was successfully introduced into vehicle trajectory prediction, and improved the accuracy of vehicle trajectory prediction. In conclusion, the trace Kalman filtering algorithm can be well applied to the running trajectory prediction of formation trains, and this method can also effectively improve the prediction accuracy and stability of trajectory prediction.…”

Section: Introductionmentioning

confidence: 99%

Research on virtual coupling railway safety protection based on track prediction

Zhang

2023

Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022)

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Virtual Coupling railway technology allows trains to conduct dynamic formation and reconciliation according to the actual passenger transport needs. The trains within the formation operate at small intervals close to the length of mechanical hooks, realizing local ultra-high density driving and collaborative control. In order to ensure that the train can also operate safely when the location and speed information is missing, this paper proposes to predict the train track and perceive the future train operation state in advance to realize the safe operation. Firstly, the existing urban rail train braking model is analyzed, and the highly accurate train braking model is identified by the improved Particle Swarm (PSO) algorithm; then after analyzing Long Short-Term Memory (LSTM) and Unscented Kalman Filter (UKF) respectively, the Virtual Coupling train track prediction algorithm based on LSTM and UKF is proposed. Finally, the actual operation data construction environment, simulation prediction and verification. The simulation experiment results show that: compared with the simple LSTM trajectory prediction, the formation train trajectory prediction model based on LSTM-UKF can further reduce the prediction error and instability, which is conducive to the safe operation of the virtual formation train.

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Maximum Likelihood Multiple Model Filtering for Path Prediction in Intelligent Transportation Systems

Cited by 8 publications

References 18 publications

Video Abnormal Behavior Recognition and Trajectory Prediction Based on Lightweight Skeleton Feature Extraction

Video Abnormal Behavior Recognition and Trajectory Prediction Based on Lightweight Skeleton Feature Extraction

Prediction techniques of spatiotemporal trajectory data: progress and framework

Research on virtual coupling railway safety protection based on track prediction

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