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Driver attention prediction is becoming an essential research problem in human-like driving systems.
C. Koch and S. Ullman, “Shifts in selective visual attention: Towards the underlying neural circuitry,”
1985
Earlier work this paper cites.
J. S. Perry and W. S. Geisler, “Gaze-contingent real-time simulation of arbitrary visual fields,”
2002
Earlier work this paper cites.
L. Itti, “Automatic foveation for video compression using a neurobiological model of visual attention,”
2004
Earlier work this paper cites.
T. Judd, K. A. Ehinger, F. Durand, and A. Torralba, “Learning to predict where humans look,” in
2009
Earlier work this paper cites.
P. K. Mital, T. J. Smith, R. L. Hill, and J. M. Henderson, “Clustering of gaze during dynamic scene viewing is predicted by motion,”
2011
Earlier work this paper cites.
Z. Wan, J. He, and A. Voisine, “An attention level monitoring and alarming system for the driver fatigue in the pervasive environment,” in
2013
Earlier work this paper cites.
A. Borji, H. R. Tavakoli, D. N. Sihite, and L. Itti, “Analysis of scores, datasets, and models in visual saliency prediction,” in
2013
Earlier work this paper cites.
R. D. Ledesma, S. A. Montes, F. M. Poo, and M. F. Lopez-Ramon, “Measuring individual differences in driver inattention: Further validation of the attention-related driving errors scale,”
2015
Earlier work this paper cites.
M. Cheng, N. J. Mitra, X. Huang, P. H. S. Torr, and S. Hu, “Global contrast based salient region detection,”
2015
Earlier work this paper cites.
S. Mathe and C. Sminchisescu, “Actions in the eye: Dynamic gaze datasets and learnt saliency models for visual recognition,”
2015
Earlier work this paper cites.
X. Shi, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” in
2015
Earlier work this paper cites.
X. Huang, C. Shen, X. Boix, and Q. Zhao, “Salicon: Reducing the semantic gap in saliency prediction by adapting deep neural networks,” in
2015
Earlier work this paper cites.
S. Jha and C. Busso, “Analyzing the relationship between head pose and gaze to model driver visual attention,” in
2016
Earlier work this paper cites.
T. Deng, K. Yang, Y. Li, and H. Yan, “Where does the driver look? top-down-based saliency detection in a traffic driving environment,”
2016
Earlier work this paper cites.
J. Zhang and S. Sclaroff, “Exploiting surroundedness for saliency detection: A boolean map approach,”
2016
Earlier work this paper cites.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in
2016
Earlier work this paper cites.
M. Cornia, L. Baraldi, G. Serra, and R. Cucchiara, “A deep multi-level network for saliency prediction,” in
2016
Earlier work this paper cites.
S. S. Kruthiventi, K. Ayush, and R. V. Babu, “Deepfix: A fully convolutional neural network for predicting human eye fixations,”
2017
Earlier work this paper cites.
D. Wang, X. Hou, J. Xu, S. Yue, and C.-L. Liu, “Traffic sign detection using a cascade method with fast feature extraction and saliency test,”
2017
Earlier work this paper cites.
S. J. Zabihi, S. M. Zabihi, S. S. Beauchemin, and M. A. Bauer, “Detection and recognition of traffic signs inside the attentional visual field of drivers,” in
2017
Earlier work this paper cites.
D. Wang, X. Hou, J. Xu, S. Yue, and C. Liu, “Traffic sign detection using a cascade method with fast feature extraction and saliency test,”
2017
Cited alongside, same era.
J. Schwehr and V. Willert, “Driver’s gaze prediction in dynamic automotive scenes,” in
2017
Cited alongside, same era.
A. Palazzi, F. Solera, S. Calderara, S. Alletto, and R. Cucchiara, “Learning where to attend like a human driver,” in
2017
Cited alongside, same era.
2017
Cited alongside, same era.
E. Ohn-Bar and M. M. Trivedi, “Are all objects equal? deep spatio-temporal importance prediction in driving videos,”
2017
Cited alongside, same era.
M. Guangyu Li, B. Jiang, Z. Che, X. Shi, M. Liu, Y. Meng, J. Ye, and Y. Liu, “Dbus: Human driving behavior understanding system,” in
2019
Closest in time.
2019
Closest in time.
J. G. Gaspar and C. Carney, “The effect of partial automation on driver attention: A naturalistic driving study,”
2019
Closest in time.
J. Fang, D. Yan, J. Qiao, J. Xue, H. Wang, and S. Li, “DADA-2000: can driving accident be predicted by driver attention? analyzed by A benchmark,” in
2019
Closest in time.
W. Wang, J. Shen, J. Xie, M.-M. Cheng, H. Ling, and A. Borji, “Revisiting video saliency prediction in the deep learning era,”
2019
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
L. Fridman, “Human-centered autonomous vehicle systems: Principles of effective shared autonomy,”
2018
Cited alongside, same era.
M. L. Cunningham and M. A. Regan, “Driver distraction and inattention,” in
2018
Cited alongside, same era.
2018
Cited alongside, same era.
A. Palazzi, D. Abati, F. Solera, R. Cucchiara
2018
Cited alongside, same era.
2019
Closest in time.
R. Cong, J. Lei, H. Fu, M. Cheng, W. Lin, and Q. Huang, “Review of visual saliency detection with comprehensive information,”
2019
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C. Chen, G. Wang, C. Peng, X. Zhang, and H. Qin, “Improved robust video saliency detection based on long-term spatial-temporal information,”
2019
Closest in time.
Q. Lai, W. Wang, H. Sun, and J. Shen, “Video saliency prediction using spatiotemporal residual attentive networks,”
2019
Closest in time.
Y. Xia, “Driver eye movements and the application in autonomous driving,” Ph.D. dissertation, UC Berkeley, 2019
2019
Closest in time.
D. Abati, A. Porrello, S. Calderara, and R. Cucchiara, “Latent space autoregression for novelty detection,” in
2019
Closest in time.
Y. Yao, M. Xu, Y. Wang, D. J. Crandall, and E. M. Atkins, “Unsupervised traffic accident detection in first-person videos,” in
2019
Closest in time.
2019
Closest in time.
K. Zhang and Z. Chen, “Video saliency prediction based on spatial-temporal two-stream network,”
2019
Closest in time.
T. Deng, H. Yan, L. Qin, T. Ngo, and B. S. Manjunath, “How do drivers allocate their potential attention? driving fixation prediction via convolutional neural networks,”
2020
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Q. Lai, W. Wang, H. Sun, and J. Shen, “Video saliency prediction using spatio-temporal residual attentive networks,”
2020
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C. Chen, G. Wang, C. Peng, X. Zhang, and H. Qin, “Improved robust video saliency detection based on long-term spatial-temporal information,”
2020
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X. Wu, Z. Wu, J. Zhang, L. Ju, and S. Wang, “Salsac: A video saliency prediction model with shuffled attentions and correlation-based convlstm,” in
2020
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A. Veit and S. J. Belongie, “Convolutional networks with adaptive inference graphs,”
2020
Closest in time.
S. Li, J. Fang, H. Xu, and J. Xue, “Video frame prediction by deep multi-branch mask network,”
2020
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