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Pedestrian crossing prediction has been a topic of active research, resulting in many new algorithmic solutions.
P. R. G. Cadena, M. Yang, Y. Qian, and C. Wang, “Pedestrian graph: Pedestrian crossing prediction based on 2d pose estimation and graph convolutional networks,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) , 2019, pp. 2000–2005
2005
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J. Demšar, “Statistical comparisons of classifiers over multiple data sets,” The Journal of Machine Learning Research , vol. 7, pp. 1–30, 2006
2006
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2014
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A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2014, pp. 1725–1732
2014
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2014
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O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. , “Imagenet large scale visual recognition challenge,” International journal of computer vision , vol. 115, no. 3, pp. 211–252, 2015
2015
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X. Shi, Z. Chen, H. Wang, D. Y. Yeung, W. K. Wong, and W. C. Woo, “Convolutional lstm network: A machine learning approach for precipitation nowcasting,” Advances in neural information processing systems , vol. 2015, pp. 802–810, 2015
2015
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D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 4489–4497
2015
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J. Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, “Beyond short snippets: Deep networks for video classification,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 4694–4702
2015
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M. P. Naeini, G. Cooper, and M. Hauskrecht, “Obtaining well calibrated probabilities using bayesian binning,” in Twenty-Ninth AAAI Conference on Artificial Intelligence , 2015
2015
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
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2016
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Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in international conference on machine learning . PMLR, 2016, pp. 1050–1059
2016
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A. Rasouli, I. Kotseruba, and J. K. Tsotsos, “Are they going to cross? a benchmark dataset and baseline for pedestrian crosswalk behavior,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2017, pp. 206–213
2017
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J. Carreira and A. Zisserman, “Quo vadis, action recognition? a new model and the kinetics dataset,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 4724–4733
2017
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C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On calibration of modern neural networks,” in International Conference on Machine Learning . PMLR, 2017, pp. 1321–1330
2017
Earlier work this paper cites.
A. Rasouli, I. Kotseruba, and J. K. Tsotsos, “Are they going to cross? a benchmark dataset and baseline for pedestrian crosswalk behavior,” in The IEEE International Conference on Computer Vision (ICCV) Workshops , Oct 2017
2017
Earlier work this paper cites.
Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, “Realtime multi-person 2d pose estimation using part affinity fields,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 7291–7299
2017
Cited alongside, same era.
B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Cited alongside, same era.
P. Stock and M. Cisse, “Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 498–512
2018
Cited alongside, same era.
D. Varytimidis, F. Alonso-Fernandez, B. Duran, and C. Englund, “Action and intention recognition of pedestrians in urban traffic,” in 2018 14th International conference on signal-image technology & internet-based systems (SITIS) . IEEE, 2018, pp. 676–682
2018
Cited alongside, same era.
I. Kotseruba, A. Rasouli, and J. K. Tsotsos, “Do they want to cross? understanding pedestrian intention for behavior prediction,” in 2020 IEEE Intelligent Vehicles Symposium (IV) , 2020, pp. 1688–1693
2020
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S. Bapu Sridhar and A. Moosakhanian, “Pedestrian intent prediction using deep machine learning,” 2020
2020
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2020
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P. Guo, Z. Xue, L. R. Long, and S. Antani, “Cross-dataset evaluation of deep learning networks for uterine cervix segmentation,” Diagnostics , vol. 10, no. 1, p. 44, 2020
2020
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O. Ghori, R. Mackowiak, M. Bautista, N. Beuter, L. Drumond, F. Diego, and B. Ommer, “Learning to forecast pedestrian intention from pose dynamics,” in 2018 IEEE Intelligent Vehicles Symposium (IV) , 2018, pp. 1277–1284
2018
Cited alongside, same era.
A. Bhattacharyya, M. Fritz, and B. Schiele, “Long-term on-board prediction of people in traffic scenes under uncertainty,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 4194–4202
2018
Cited alongside, same era.
J. Heo, H. B. Lee, S. Kim, J. Lee, K. J. Kim, E. Yang, and S. J. Hwang, “Uncertainty-aware attention for reliable interpretation and prediction,” in Proceedings of the 32nd International Conference on Neural Information Processing Systems , 2018, pp. 917–926
2018
Cited alongside, same era.
2018
Cited alongside, same era.
K. Saleh, M. Hossny, and S. Nahavandi, “Real-time intent prediction of pedestrians for autonomous ground vehicles via spatio-temporal densenet,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 9704–9710
2019
Cited alongside, same era.
A. Marginean, R. Brehar, and M. Negru, “Understanding pedestrian behaviour with pose estimation and recurrent networks,” in 2019 6th International Symposium on Electrical and Electronics Engineering (ISEEE) , 2019, pp. 1–6
2019
Cited alongside, same era.
Y. Ovadia, E. Fertig, J. Ren, Z. Nado, D. Sculley, S. Nowozin, J. Dillon, B. Lakshminarayanan, and J. Snoek, “Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift,” Advances in Neural Information Processing Systems , vol. 32, pp. 13 991–14 002, 2019
2019
Cited alongside, same era.
A. Rasouli, I. Kotseruba, T. Kunic, and J. K. Tsotsos, “Pie: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction,” in ICCV , 2019
2019
Cited alongside, same era.
Y. Ding, J. Liu, J. Xiong, and Y. Shi, “Revisiting the evaluation of uncertainty estimation and its application to explore model complexity-uncertainty trade-off,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 4–5
2020
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I. Kotseruba, A. Rasouli, and J. K. Tsotsos, “Benchmark for evaluating pedestrian action prediction,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2021, pp. 1258–1268
2021
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C. Northcutt, L. Jiang, and I. Chuang, “Confident learning: Estimating uncertainty in dataset labels,” Journal of Artificial Intelligence Research , vol. 70, pp. 1373–1411, 2021
2021
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2021
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J. Gesnouin, S. Pechberti, B. Stanciulcscu, and F. Moutarde, “Trouspi-net: Spatio-temporal attention on parallel atrous convolutions and u-grus for skeletal pedestrian crossing prediction,” in 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021) . IEEE, 2021, pp. 01–07
2021
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2021
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J. Lorenzo, I. P. Alonso, R. Izquierdo, A. L. Ballardini, Á. H. Saz, D. F. Llorca, and M. Á. Sotelo, “Capformer: Pedestrian crossing action prediction using transformer,” Sensors , vol. 21, no. 17, p. 5694, 2021
2021
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2021
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A. Singh and U. Suddamalla, “Multi-input fusion for practical pedestrian intention prediction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 2304–2311
2021
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D. Zhang, F. Shi, Y. Meng, Y. Xu, X. Xiao, and W. Li, “Pedestrian intention prediction via depth augmented scene restoration,” in 2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) . IEEE, 2021, pp. 1–6
2021
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I. Hasan, S. Liao, J. Li, S. U. Akram, and L. Shao, “Generalizable pedestrian detection: The elephant in the room,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 11 328–11 337
2021
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2021
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P. Gujjar and R. Vaughan, “Classifying pedestrian actions in advance using predicted video of urban driving scenes,” in 2019 International Conference on Robotics and Automation (ICRA) , 2019, pp. 2097–2103
2097
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