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Spatial-Temporal-Sp...
Spatial-Temporal-Spectral LSTM: A Transferable Model for Pedestrian Trajectory Prediction
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- Zhang, Chi (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU),Chalmers tekniska högskola,Chalmers University of Technology
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- Ni, Zhongjun (författare)
- Linköpings universitet,Linköping University
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- Berger, Christian, 1980 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU),Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2023
- 2023
- Engelska.
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Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858.
- Relaterad länk:
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https://gup.ub.gu.se...
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visa fler...
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https://doi.org/10.1...
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https://research.cha...
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Abstract
Ämnesord
Stäng
- Predicting the trajectories of pedestrians is critical for developing safe advanced driver assistance systems and autonomous driving systems. Most existing models for pedestrian trajectory prediction focused on a single dataset without considering the transferability to other previously unseen datasets. This leads to poor performance on new unseen datasets and hinders leveraging off-the-shelf labeled datasets and models. In this paper, we propose a transferable model, namely the “Spatial-Temporal-Spectral (STS) LSTM” model, that represents the motion pattern of pedestrians with spatial, temporal, and spectral domain information. Quantitative results and visualizations indicate that our proposed spatial-temporal-spectral representation enables the model to learn generic motion patterns and improves the performance on both source and target datasets. We reveal the transferability of three commonly used network structures, including long short-term memory networks (LSTMs), convolutional neural networks (CNNs), and Transformers, and employ the LSTM structure with negative log-likelihood loss in our model since it has the best transferability. The proposed STS LSTM model demonstrates good prediction accuracy when transferring to target datasets without any prior knowledge, and has a faster inference speed compared to the state-of-the-art models. Our work addresses the gap in learning knowledge from source datasets and transferring it to target datasets in the field of pedestrian trajectory prediction, and enables the reuse of publicly available off-the-shelf datasets.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Computational modeling
- Data models
- deep learning
- Fourier transform
- Intelligent vehicles
- Pedestrian trajectory prediction
- Predictive models
- spectral representation
- Task analysis
- Trajectory
- transferable models
- Transformers
- spectral representation
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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