![](bgb.png)
He, P., Wu, A., Huang, X., Rangarajan, A., and Ranka,
S. (2020). Video-based machine learning system for
commodity classification. In 6th International Con-
ference on Vehicle Technology and Intelligent Trans-
port Systems (VEHITS 2020).
Huang, X., He, P., Rangarajan, A., and Ranka, S. (2020). In-
telligent intersection: Two-stream convolutional net-
works for real-time near-accident detection in traffic
video. ACM Trans. Spatial Algorithms Syst., 6(2).
Jiansheng, F. et al. (2014). Vision-based real-time traf-
fic accident detection. In Proceeding of the 11th
World Congress on Intelligent Control and Automa-
tion, pages 1035–1038. IEEE.
Kalman, R. E. (1960). A new approach to linear filtering
and prediction problems. Journal of basic Engineer-
ing, 82(1):35–45.
Kamijo, S., Matsushita, Y., Ikeuchi, K., and Sakauchi, M.
(2000). Traffic monitoring and accident detection at
intersections. IEEE Transactions on Intelligent Trans-
portation Systems, 1(2):108–118.
Lee, Y., Jeong, J., Yun, J., Cho, W., and Yoon, K.-J. (2019).
Spherephd: Applying cnns on a spherical polyhedron
representation of 360deg images. In Proceedings of
the IEEE Conference on Computer Vision and Pattern
Recognition, pages 9181–9189.
Li, X., Zhang, B., Sander, P. V., and Liao, J. (2019). Blind
geometric distortion correction on images through
deep learning. In Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition, pages
4855–4864.
Liu, X., Liu, W., Ma, H., and Fu, H. (2016). Large-scale ve-
hicle re-identification in urban surveillance videos. In
2016 IEEE International Conference on Multimedia
and Expo (ICME), pages 1–6. IEEE.
Maaloul, B., Taleb-Ahmed, A., Niar, S., Harb, N., and
Valderrama, C. (2017). Adaptive video-based algo-
rithm for accident detection on highways. In 2017
12th IEEE International Symposium on Industrial
Embedded Systems (SIES), pages 1–6. IEEE.
Ohe, I., Kawashima, H., Kojima, M., and Kaneko, Y.
(1995). A method for automatic detection of traf-
fic incidents using neural networks. In Pacific Rim
TransTech Conference. 1995 Vehicle Navigation and
Information Systems Conference Proceedings. 6th In-
ternational VNIS. A Ride into the Future, pages 231–
235. IEEE.
Redmon, J. and Farhadi, A. (2017). YOLO9000: better,
faster, stronger. In Proceedings of the IEEE Con-
ference on Computer Vision and Pattern Recognition,
pages 7263–7271.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-
CNN: Towards real-time object detection with region
proposal networks. In Advances in Neural Informa-
tion Processing Systems, pages 91–99.
Sadeky, S., Al-Hamadiy, A., Michaelisy, B., and Sayed, U.
(2010). Real-time automatic traffic accident recogni-
tion using hfg. In 2010 20th International Conference
on Pattern Recognition, pages 3348–3351. IEEE.
Saunier, N., Sayed, T., and Ismail, K. (2010). Large-scale
automated analysis of vehicle interactions and colli-
sions. Transportation Research Record: Journal of
the Transportation Research Board, (2147):42–50.
Shah, A., Lamare, J. B., Anh, T. N., and Hauptmann, A.
(2018). Accident forecasting in cctv traffic camera
videos. arXiv preprint arXiv:1809.05782.
Singh, D. and Mohan, C. K. (2018). Deep spatio-temporal
representation for detection of road accidents using
stacked autoencoder. IEEE Transactions on Intelli-
gent Transportation Systems.
Srinivasan, D., Cheu, R. L., and Poh, Y. P. (2001). Hy-
brid fuzzy logic-genetic algorithm technique for au-
tomated detection of traffic incidents on freeways. In
ITSC 2001. 2001 IEEE Intelligent Transportation Sys-
tems. Proceedings (Cat. No. 01TH8585), pages 352–
357. IEEE.
Srinivasan, D., Jin, X., and Cheu, R. L. (2004). Evalua-
tion of adaptive neural network models for freeway
incident detection. IEEE Transactions on Intelligent
Transportation Systems, 5(1):1–11.
Srinivasan, D., Loo, W. H., and Cheu, R. L. (2003). Traffic
incident detection using particle swarm optimization.
In Proceedings of the 2003 IEEE Swarm Intelligence
Symposium. SIS’03 (Cat. No. 03EX706), pages 144–
151. IEEE.
Sultani, W., Chen, C., and Shah, M. (2018). Real-world
anomaly detection in surveillance videos. Center for
Research in Computer Vision (CRCV), University of
Central Florida (UCF).
Tang, Z., Naphade, M., Liu, M.-Y., Yang, X., Birchfield,
S., Wang, S., Kumar, R., Anastasiu, D., and Hwang,
J.-N. (2019). Cityflow: A city-scale benchmark
for multi-target multi-camera vehicle tracking and re-
identification. In The IEEE Conference on Computer
Vision and Pattern Recognition (CVPR).
Ullah, H., Ullah, M., Afridi, H., Conci, N., and De Natale,
F. G. (2015). Traffic accident detection through a hy-
drodynamic lens. In 2015 IEEE International Confer-
ence on Image Processing (ICIP), pages 2470–2474.
IEEE.
Wei, J., Li, C.-F., Hu, S.-M., Martin, R. R., and Tai, C.-
L. (2011). Fisheye video correction. IEEE Trans-
actions on Visualization and Computer Graphics,
18(10):1771–1783.
Wojke, N., Bewley, A., and Paulus, D. (2017). Simple on-
line and realtime tracking with a deep association met-
ric. In 2017 IEEE International Conference on Image
Processing (ICIP), pages 3645–3649. IEEE.
Xia, S., Xiong, J., Liu, Y., and Li, G. (2015). Vision-
based traffic accident detection using matrix approx-
imation. In 2015 10th Asian Control Conference
(ASCC), pages 1–5. IEEE.
Yin, X., Wang, X., Yu, J., Zhang, M., Fua, P., and Tao, D.
(2018). Fisheyerecnet: A multi-context collaborative
deep network for fisheye image rectification. In Pro-
ceedings of the European Conference on Computer Vi-
sion (ECCV), pages 469–484.
Zeng, D., Xu, J., and Xu, G. (2008). Data fusion for traffic
incident detection using ds evidence theory with prob-
abilistic svms. Journal of computers, 3(10):36–43.
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