The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior
Abstract
Designing autonomous vehicles suitable for urban environments remains an unresolved problem. One of the major dilemmas faced by autonomous cars is how to understand the intention of other road users and communicate with them. The existing datasets do not provide the necessary means for such higher level analysis of traffic scenes. With this in mind, we introduce a novel dataset which in addition to providing the bounding box information for pedestrian detection, also includes the behavioral and contextual annotations for the scenes. This allows combining visual and semantic information for better understanding of pedestrians' intentions in various traffic scenarios. We establish baseline approaches for analyzing the data and show that combining visual and contextual information can improve prediction of pedestrian intention at the point of crossing by at least 20%.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150 · cited 2×
“For this purpose we use pre-trained AlexNet on two large image datasets, ImageNet [5] and places, and both datasets combined [44].”
MethodYou'll never walk alone: Modeling social behavior for multi-target tracking
Pellegrini, S., Ess, A., Schindler, K., & van Gool, L. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459260 · Cited by 1,683 · cited 1×
“The common approach to remedy the problem of predicting road users behavior is to employ dynamic factors, such as trajectory [3] or velocity [30], or the expected final goal of pedestrians [18, 31].”
BackgroundMultispectral pedestrian detection: Benchmark dataset and baseline
Hwang, S., Park, J., Kim, N., Choi, Y., & Kweon, I. S. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298706 · Cited by 1,249
Is Faster R-CNN Doing Well for Pedestrian Detection?
Zhang, L., Lin, L., Liang, X., & He, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_28 · Cited by 852
Cited by
Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice
Rasouli, A., & Tsotsos, J. K. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2901817 · Cited by 855
Self-citationMez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge
George, A., Ravindran, A., Mendieta, M., & Tabkhi, H. (2021) · IEEE Access · doi:10.1109/access.2021.3055775 · Cited by 73
“We use two publicly available video datasets - JAAD [19] and DukeMTMC [20] for latency characterization.”
MethodDetecting 32 Pedestrian Attributes for Autonomous Vehicles
Mordan, T., Cord, M., Perez, P., & Alahi, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3107587 · Cited by 36
Metadata
- Authors
- Rasouli, A., Kotseruba, I., & Tsotsos, J. K.
- Venue
- 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 206-213
- ISSN
- 2473-9936 (ISSN-L)
- OpenAlex
- W2771583656
- Cited by
- 425
- References
- 9
- FWCI
- 19.55
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Rasouli, A., Kotseruba, I., & Tsotsos, J. K. (2017). Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). https://doi.org/10.1109/iccvw.2017.33
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