CSR: Cascade Conditional Variational Auto Encoder with Socially-aware Regression for Pedestrian Trajectory Prediction
•The proposed trajectory prediction method consists of a cascaded CVAE module and a socially aware regression module.•The cascaded CVAE module decouples and balances the loss function with respect to time steps and minimizes the losses at every time steps independently.•The socially aware regression...
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| Veröffentlicht in: | Pattern recognition Jg. 133; S. 109030 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.01.2023
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| ISSN: | 0031-3203, 1873-5142 |
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| Abstract | •The proposed trajectory prediction method consists of a cascaded CVAE module and a socially aware regression module.•The cascaded CVAE module decouples and balances the loss function with respect to time steps and minimizes the losses at every time steps independently.•The socially aware regression module corrects the predictions by checking the compatibility between the interaction coding and the crude predicted trajectories.
Pedestrian trajectory prediction is a key technology in many real applications such as video surveillance, social robot navigation, and autonomous driving, and significant progress has been made in this research topic. However, there remain two limitations of previous studies. First, the losses of the last time steps are heavier weighted than that of the beginning time steps in the objective function at the learning stage, causing the prediction errors generated at the beginning to accumulate to large errors at the last time steps at the inference stage. Second, the prediction results of multiple pedestrians in the prediction horizon might be socially incompatible with the interactions modeled by past trajectories. To overcome these limitations, this work proposes a novel trajectory prediction method called CSR, which consists of a cascaded conditional variational autoencoder (CVAE) module and a socially-aware regression module. The CVAE module estimates the future trajectories in a cascaded sequential manner. Specifically, each CVAE concatenates the past trajectories and the predicted location points so far as the input and predicts the adjacent location at the following time step. The socially-aware regression module generates offsets from the estimated future trajectories to produce the corrected predictions, which are more reasonable and accurate than the estimated trajectories. Experiments results demonstrate that the proposed method exhibits significant improvements over state-of-the-art methods on the Stanford Drone Dataset (SDD) and the ETH/UCY dataset of approximately 38.0% and 22.2%, respectively. The code is available at https://github.com/zhouhao94/CSR. |
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| AbstractList | •The proposed trajectory prediction method consists of a cascaded CVAE module and a socially aware regression module.•The cascaded CVAE module decouples and balances the loss function with respect to time steps and minimizes the losses at every time steps independently.•The socially aware regression module corrects the predictions by checking the compatibility between the interaction coding and the crude predicted trajectories.
Pedestrian trajectory prediction is a key technology in many real applications such as video surveillance, social robot navigation, and autonomous driving, and significant progress has been made in this research topic. However, there remain two limitations of previous studies. First, the losses of the last time steps are heavier weighted than that of the beginning time steps in the objective function at the learning stage, causing the prediction errors generated at the beginning to accumulate to large errors at the last time steps at the inference stage. Second, the prediction results of multiple pedestrians in the prediction horizon might be socially incompatible with the interactions modeled by past trajectories. To overcome these limitations, this work proposes a novel trajectory prediction method called CSR, which consists of a cascaded conditional variational autoencoder (CVAE) module and a socially-aware regression module. The CVAE module estimates the future trajectories in a cascaded sequential manner. Specifically, each CVAE concatenates the past trajectories and the predicted location points so far as the input and predicts the adjacent location at the following time step. The socially-aware regression module generates offsets from the estimated future trajectories to produce the corrected predictions, which are more reasonable and accurate than the estimated trajectories. Experiments results demonstrate that the proposed method exhibits significant improvements over state-of-the-art methods on the Stanford Drone Dataset (SDD) and the ETH/UCY dataset of approximately 38.0% and 22.2%, respectively. The code is available at https://github.com/zhouhao94/CSR. |
| ArticleNumber | 109030 |
| Author | Yang, Xu Zhou, Hao Ren, Dongchun Huang, Hai Fan, Mingyu |
| Author_xml | – sequence: 1 givenname: Hao surname: Zhou fullname: Zhou, Hao organization: National Key Laboratory of Science and Technology of Underwater Vehicle, Harbin Engineering University, Harbin, China – sequence: 2 givenname: Dongchun surname: Ren fullname: Ren, Dongchun organization: Research Center for Autonomous Vehicles, Meituan, Beijing, China – sequence: 3 givenname: Xu orcidid: 0000-0003-0553-4581 surname: Yang fullname: Yang, Xu organization: State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, China – sequence: 4 givenname: Mingyu orcidid: 0000-0002-0492-4708 surname: Fan fullname: Fan, Mingyu email: fanmingyu@wzu.edu.cn organization: Research Center for Autonomous Vehicles, Meituan, Beijing, China – sequence: 5 givenname: Hai surname: Huang fullname: Huang, Hai email: haihus@163.com organization: National Key Laboratory of Science and Technology of Underwater Vehicle, Harbin Engineering University, Harbin, China |
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| Cites_doi | 10.7551/mitpress/3206.001.0001 10.1016/j.patcog.2019.04.025 10.1162/neco.1997.9.8.1735 10.1016/j.patcog.2020.107800 10.20485/jsaeijae.7.AVEC14_53 10.1109/TITS.2008.2011691 10.1016/j.patcog.2021.108252 10.1109/LRA.2018.2852793 10.1016/j.neucom.2021.03.024 10.1109/TPAMI.2020.3038217 |
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| Keywords | 99-00 Pedestrian trajectory prediction Conditional variational autoencoder (CVAE) Socially-aware model 11-01 |
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| SubjectTerms | Conditional variational autoencoder (CVAE) Pedestrian trajectory prediction Socially-aware model |
| Title | CSR: Cascade Conditional Variational Auto Encoder with Socially-aware Regression for Pedestrian Trajectory Prediction |
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