A Location-Velocity-Temporal Attention LSTM Model for Pedestrian Trajectory Prediction

Xue, Hao; Huynh, Du Q.; Reynolds, Mark

doi:10.1109/access.2020.2977747

Cited by 31 publications

(9 citation statements)

References 45 publications

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“…For example, the soft attention score is calculated based on the hidden state, and the pairwise speed correlation is calculated [42,45], using an attention model that combines ' soft attention ' and ' hard attention '. Based on LSTM 's position-velocity-timeattention model [47], two temporal attention mechanisms are designed to calculate the hidden state vectors of the position and velocity LSTM layers. The application of attention mechanism enhances the interaction between pedestrians and improves the credibility of trajectory prediction.…”

Section: Trajectory Prediction Methods Based On Rnnmentioning

confidence: 99%

Review of pedestrian trajectory prediction methods

Tan¹,

Liu²,

Zhang³

et al. 2022

FCIS

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Urban driverless vehicles will inevitably interact with pedestrians in the process of driving. In order to avoid path conflict with pedestrians, the research on pedestrian trajectory prediction is of great significance. This paper mainly summarizes the technical classification and research status of pedestrian trajectory prediction at this stage in detail. According to the different modeling methods, the existing trajectory prediction methods are divided into shallow learning-based trajectory prediction methods and depth learning based trajectory prediction methods. The advantages and disadvantages of the depth learning based trajectory prediction methods are compared, and the current mainstream pedestrian trajectory prediction public dataset is summarized, and the performance of the mainstream pedestrian trajectory prediction methods is compared in the dataset. Finally, the challenges and development trend of pedestrian trajectory prediction at this stage are prospected.

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Section: Trajectory Prediction Methods Based On Rnnmentioning

confidence: 99%

Review of pedestrian trajectory prediction methods

Tan¹,

Liu²,

Zhang³

et al. 2022

FCIS

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show abstract

“…different comprehensions of human motion, resulting in diverse manifestations of human motion prediction. These have been inspired by various classifications of prediction methods: 1) 2D Motion trajectory prediction: it mainly predicts motion trajectories for human or moving devices [42,43,97,100,101,84,85] in a 2D plane. 2) Video prediction: it focuses on the motion prediction on the video frames [109,90,69,105].…”

Section: Scopementioning

confidence: 99%

3D Human Motion Prediction: A Survey

Lyu¹,

Chen²,

Liu³

et al. 2022

Preprint

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3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors. Due to the increasing development and understanding of Deep Neural Networks (DNNs) and the availability of large-scale human motion datasets, the human motion prediction has been remarkably advanced with a surge of interest among academia and industrial community. In this context, a comprehensive survey on 3D human motion prediction is conducted for the purpose of retrospecting and analyzing relevant works from existing released literature. In addition, a pertinent taxonomy is constructed to categorize these existing approaches for 3D human motion prediction. In this survey, relevant methods are categorized into three categories: human pose representation, network structure design, and prediction target. We systematically review all relevant journal and conference papers in the field of human motion prediction since 2015, which are presented in detail based on proposed categorizations in this survey. Furthermore, the outline for the public benchmark datasets, evaluation criteria, and performance comparisons are respectively presented in this paper. The limitations of the state-of-the-art methods are discussed as well, hoping for paving the way for future explorations.

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“…e high-definition camera is used to capture the athlete's motion process, obtain the athlete's motion video image, and complete the motion trajectory analysis of the athlete's human parts on this basis [10,11]. ) is similarity measure, as to complete the motion trajectory expression of a single part of the human.…”

Section: Analysis Of Human Motion Trajectory Of Athletesmentioning

confidence: 99%

Segmentation for Human Motion Injury Ultrasound Medical Images Using Deep Feature Fusion

Sun

Liu

2022

Mathematical Problems in Engineering

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Image processing technology assists physicians in the analysis of athletes’ human motion injuries, not only to improve the accuracy of athletes’ injury detection but also to improve the localization and recognition of injury locations. It is important to accurately segment human motion injury ultrasound medical images. To address many problems such as poor effect of traditional ultrasonic medical image segmentation algorithm for a sports injury. Therefore, we propose a segmentation algorithm for human motion injury ultrasound medical images using deep feature fusion. First, the accurate estimated value of human posture is extracted and combined with image texture features and image gray value as the target feature value of the ultrasonic medical image of human motion injury. Second, the image features are deeply fused by an adaptive fusion algorithm to enhance the image resolution. Finally, the best segmentation value of the image is obtained by the trained support vector machine to realize the accurate segmentation of human motion injury ultrasonic medical image. The results show that the average accuracy of the posture accurate estimation of the proposed algorithm is 95.97%; the segmentation time of the human motion injury ultrasound medical image of the proposed algorithm is below 150 ms; and the convergence of the algorithm is completed when the number of iterations is 3. The maximum segmentation error rate is 2.68%. The image segmentation effect is consistent with the ideal target segmentation effect. The proposed algorithm has important application value in the field of ultrasonic medical diagnosis of sports injury.

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A Location-Velocity-Temporal Attention LSTM Model for Pedestrian Trajectory Prediction

Cited by 31 publications

References 45 publications

Review of pedestrian trajectory prediction methods

Review of pedestrian trajectory prediction methods

3D Human Motion Prediction: A Survey

Segmentation for Human Motion Injury Ultrasound Medical Images Using Deep Feature Fusion

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