Fetching the paper…
Reading the bibliography…
Video anomaly detection (VAD) has been extensively studied.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation (1997)
1997
Earlier work this paper cites.
Li, W., Mahadevan, V., Vasconcelos, N.: Anomaly detection and localization in crowded scenes. TPAMI (2013)
2013
Earlier work this paper cites.
Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 fps in matlab. In: ICCV (2013)
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: CVPR (2014)
2014
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: NeurIPS (2014)
2014
Earlier work this paper cites.
Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks. In: ICCV (2015)
2015
Earlier work this paper cites.
Chan, F.H., Chen, Y.T., Xiang, Y., Sun, M.: Anticipating accidents in dashcam videos. In: ACCV (2016)
2016
Earlier work this paper cites.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: CVPR (2016)
2016
Earlier work this paper cites.
De Geest, R., Gavves, E., Ghodrati, A., Li, Z., Snoek, C., Tuytelaars, T.: Online action detection. In: ECCV (2016)
2016
Earlier work this paper cites.
Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences. In: CVPR (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., Van Gool, L.: Temporal segment networks: Towards good practices for deep action recognition. In: ECCV (2016)
2016
Earlier work this paper cites.
Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: ECCV (2016)
2016
Earlier work this paper cites.
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016)
2016
Earlier work this paper cites.
Bakker, J., Jeppsson, H., Hannawald, L., Spitzhüttl, F., Longton, A., Tomasch, E.: Iglad-international harmonized in-depth accident data. In: ESV (2017)
2017
Cited alongside, same era.
Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: CVPR (2017)
2017
Cited alongside, same era.
Chong, Y.S., Tay, Y.H.: Abnormal event detection in videos using spatiotemporal autoencoder. In: ISNN (2017)
2017
Cited alongside, same era.
Gao, J., Yang, Z., Nevatia, R.: Red: Reinforced encoder-decoder networks for action anticipation. BMVC (2017)
2017
Cited alongside, same era.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)
2017
Cited alongside, same era.
2018
Later among the works it cites.
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. In: CVPR (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Feichtenhofer, C., Fan, H., Malik, J., He, K.: Slowfast networks for video recognition. In: ICCV (2019)
2019
Later among the works it cites.
Gao, M., Xu, M., Davis, L.S., Socher, R., Xiong, C.: StartNet: Online detection of action start in untrimmed videos. In: ICCV (2019)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: Evolution of optical flow estimation with deep networks. In: CVPR (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Luo, W., Liu, W., Gao, S.: Remembering history with convolutional lstm for anomaly detection. In: ICME (2017)
2017
Cited alongside, same era.
Luo, W., Liu, W., Gao, S.: A revisit of sparse coding based anomaly detection in stacked rnn framework. In: ICCV (2017)
2017
Cited alongside, same era.
Kim, J., Rohrbach, A., Darrell, T., Canny, J., Akata, Z.: Textual explanations for self-driving vehicles. In: ECCV (2018)
2018
Cited alongside, same era.
Liu, W., Luo, W., Lian, D., Gao, S.: Future frame prediction for anomaly detection–a new baseline. In: CVPR (2018)
2018
Cited alongside, same era.
Shou, Z., Pan, J., Chan, J., Miyazawa, K., Mansour, H., Vetro, A., Giro-i Nieto, X., Chang, S.F.: Online detection of action start in untrimmed, streaming videos. In: ECCV (2018)
2018
Cited alongside, same era.
2019
Later among the works it cites.
Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.v.d.: Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In: ICCV (2019)
2019
Later among the works it cites.
Herzig, R., Levi, E., Xu, H., Gao, H., Brosh, E., Wang, X., Globerson, A., Darrell, T.: Spatio-temporal action graph networks. In: CVPRW (2019)
2019
Later among the works it cites.
Ionescu, R.T., Khan, F.S., Georgescu, M.I., Shao, L.: Object-centric auto-encoders and dummy anomalies for abnormal event detection in video. In: CVPR (2019)
2019
Later among the works it cites.
Liu, W., Luo, W., Li, Z., Zhao, P., Gao, S.: Margin learning embedded prediction for video anomaly detection with a few anomalies. In: IJCAI (2019)
2019
Later among the works it cites.
Morais, R., Le, V., Tran, T., Saha, B., Mansour, M., Venkatesh, S.: Learning regularity in skeleton trajectories for anomaly detection in videos. In: CVPR (2019)
2019
Later among the works it cites.
Wang, J., Cherian, A.: Gods: Generalized one-class discriminative subspaces for anomaly detection. In: ICCV (2019)
2019
Later among the works it cites.
Xu, M., Gao, M., Chen, Y.T., Davis, L.S., Crandall, D.J.: Temporal recurrent networks for online action detection. In: ICCV (2019)
2019
Later among the works it cites.
Yao, Y., Xu, M., Choi, C., Crandall, D.J., Atkins, E.M., Dariush, B.: Egocentric vision-based future vehicle localization for intelligent driving assistance systems. In: ICRA (2019)
2019
Later among the works it cites.
Yao, Y., Xu, M., Wang, Y., Crandall, D.J., Atkins, E.M.: Unsupervised traffic accident detection in first-person videos. In: IROS (2019)
2019
Later among the works it cites.
Yao, Y., Atkins, E.: The smart black box: A value-driven high-bandwidth automotive event data recorder. TITS (2020)
2020
Closest in time.