Local Destination Pooling Network for Pedestrian Trajectory Prediction of Condition Endpoint

MAO Lin; XIE Yunjiao; YANG Dawei; ZHANG Rubo

doi:10.11999/JEIT210716

Volume 44 Issue 10

Oct. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(10): 3465-3475

MAO Lin, XIE Yunjiao, YANG Dawei, ZHANG Rubo. Local Destination Pooling Network for Pedestrian Trajectory Prediction of Condition Endpoint[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3465-3475. doi: 10.11999/JEIT210716

Citation:

MAO Lin, XIE Yunjiao, YANG Dawei, ZHANG Rubo. Local Destination Pooling Network for Pedestrian Trajectory Prediction of Condition Endpoint[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3465-3475. doi: 10.11999/JEIT210716

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Local Destination Pooling Network for Pedestrian Trajectory Prediction of Condition Endpoint

doi: 10.11999/JEIT210716

College of Mechanical and Electronic Engineering, Dalian Minzu University, Dalian 116600, China

Funds: The National Natural Science Foundation of China (61673084), The Natural Science Foundation of Liaoning Province (20180550866)

Received Date: 2021-07-15
Accepted Date: 2021-12-29
Rev Recd Date: 2021-12-17

Available Online: 2022-01-13

Publish Date: 2022-10-19

Abstract

Abstract

Trajectory prediction is one of the core tasks in automatic driving system. At present, trajectory prediction algorithms based on deep learning involve information representation, perception and motion reasoning of targets. Considering the problem that the existing trajectory prediction models does not take into account the social motivation of pedestrians and can not effectively predict the local destination of pedestrians in different social conditions in the scene, a Conditional Endpoint local destination Pooling NETwork (CEPNET) is proposed. The network uses conditional variational autoencoder to map out the potential distribution in space, which can study the observation of the history track probability distribution in the specific scene. And then a local feature inference algorithm is built to code the similarity features of conditional endpoint as local destination features. Finally, the interference signals in the scene are filtered out by social pooling network. At the same time, self-attention social mask is used to enhance pedestrian’s self-attention. In order to verify the reliability of each module of the algorithm, the public datasets of Walking pedestrians In busy scenarios from a BIrd eye view(BIWI) and University of CYprus multi-person trajectory (UCY) are used to conduct ablation experiments, and compared with advanced trajectory prediction algorithms such as Vanilla, Socially acceptable trajectories with Generative Adversarial Networks (SGAN) and multimodal Trajectory forecasting using Bicycle-GAN and Graph Attention networks(S-BiGAT). The experimental results on the Trajnet++ benchmark show that compared with the benchmark Vanilla algorithm, the Average Displacement Error (ADE) is reduced by 22.52%, the Final Displacement Error (FDE) is reduced by 20%, the predicted collision rate Col-I is reduced by 9.75%, and the true collision rate Col-II is reduced by 9.15%.
- Trajectory prediction,
- Conditional probability distribution,
- Destination pooling,
- Self-attention

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References(21)

References

[1]	CHEN Changan, LIU Yuejiang, KREISS S, et al. Crowd-robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning[C]. 2019 International Conference on Robotics and Automation (ICRA), Montreal, Canada, 2019: 6015–6022.
[2]	RASOULI A and TSOTSOS J K. Autonomous vehicles that interact with pedestrians: A survey of theory and practice[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(3): 900–918. doi: 10.1109/TITS.2019.2901817
[3]	BITGOOD S. An analysis of visitor circulation: Movement patterns and the general value principle[J]. Curator:The Museum Journal, 2006, 49(4): 463–475. doi: 10.1111/j.2151-6952.2006.tb00237.x
[4]	HORNI A, NAGEL K, and AXHAUSEN K W. The Multi-Agent Transport Simulation MATSim[M]. London: Ubiquity Press, 2016: 355–361.
[5]	DONG Hairong, ZHOU Min, WANG Qianling, et al. State-of-the-art pedestrian and evacuation dynamics[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(5): 1849–1866. doi: 10.1109/TITS.2019.2915014
[6]	ALAHI A, GOEL K, RAMANATHAN V, et al. Social LSTM: Human trajectory prediction in crowded spaces[C]. 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, USA, 2016: 961–971.
[7]	BISAGNO N, ZHANG Bo, and CONCI N. Group LSTM: Group trajectory prediction in crowded scenarios[C]. European Conference on Computer Vision, Munich, Germany, 2018: 213–225.
[8]	PELLEGRINI S, ESS A, SCHINDLER K, et al. You'll never walk alone: Modeling social behavior for multi-target tracking[C]. 2009 IEEE 12th International Conference on Computer Vision (ICCV), Kyoto, Japan, 2009: 261–268.
[9]	LERNER A, CHRYSANTHOU Y, and LISCHINSKI D. Crowds by example[J]. Computer Graphics Forum, 2007, 26(3): 655–664. doi: 10.1111/j.1467-8659.2007.01089.x
[10]	XUE Hao, HUYNH D Q, and REYNOLDS M. SS-LSTM: A hierarchical LSTM model for pedestrian trajectory prediction[C]. 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, USA, 2018: 1186–1194.
[11]	CHEUNG E, WONG T K, BERA A, et al. LCrowdV: Generating labeled videos for simulation-based crowd behavior learning[C]. European Conference on Computer Vision, Amsterdam, Netherlands, 2016: 709–727.
[12]	BARTOLI F, LISANTI G, BALLAN L, et al. Context-aware trajectory prediction[C]. 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China, 2018: 1941–1946.
[13]	GUPTA A, JOHNSON J, LI Feifei, et al. Social GAN: Socially acceptable trajectories with generative adversarial networks[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018: 2255–2264.
[14]	FERNANDO T, DENMAN S, SRIDHARAN S, et al. GD-GAN: Generative adversarial networks for trajectory prediction and group detection in crowds[C]. Asian Conference on Computer Vision, Perth, Australia, 2018: 314–330.
[15]	VAN DER MAATEN L. Accelerating t-SNE using tree-based algorithms[J]. The Journal of Machine Learning Research, 2014, 15(1): 3221–3245.
[16]	KOSARAJU V, SADEGHIAN A, MARTÍN-MARTÍN R, et al. Social-BiGAT: Multimodal trajectory forecasting using bicycle-GAN and graph attention networks[C]. The 33rd International Conference on Neural Information Processing Systems, Vancouver, ‎Canada, 2019: 137–146.
[17]	MANGALAM K, GIRASE H, AGARWAL S, et al. It is not the journey but the destination: Endpoint conditioned trajectory prediction[C]. European Conference on Computer Vision, Glasgow, United Kingdom, 2020: 759–776.
[18]	SALZMANN T, IVANOVIC B, CHAKRAVARTY P, et al. Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data[C]. European Conference on Computer Vision, Glasgow, United Kingdom, 2020: 683–700.
[19]	KOTHARI P, KREISS S, and ALAHI A. Human trajectory forecasting in crowds: A deep learning perspective[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7): 7386–7400. doi: 10.1109/TITS.2021.3069362
[20]	LEE N, CHOI W, VERNAZA P, et al. DESIRE: Distant future prediction in dynamic scenes with interacting agents[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2017: 336–345.
[21]	KINGMA D P and WELLING M. Auto-encoding variational Bayes[C]. International Conference on Learning Representations ICLR 2014 Conference Track (ICLR), Banff, Canada, 2014: 1–14.