Video Abnormal Event Detection and Location Based on Spatial Attention
Pages 76 - 81
References
[1]
Zeiler M.D., Fergus R. 2014. Visualizing and Understanding Convolutional Networks. In Proceedings of Computer Vision – 13th. European Conference on Computer Vision (ECCV 2014), September 6-12, 2014, Zurich, Switzerland. Springer, Cham, 818-833. https://doi.org/10.1007/978-3-319-10590-1_53
[2]
Dong Li, Jia-Bin Huang, Yali Li, Shengjin Wang and Ming-Hsuan Yang. 2016. Weakly Supervised Object Localization with Progressive Domain Adaptation. In Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (2016CVPR), June 27-30, 2016, Las Vegas, NV, USA. IEEE, New Jersey, NJ, 3512-3520. https://doi.org/ 10.1109/CVPR.2016.382
[3]
Bolei zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva and Antonio Torralba. 2014. Object detectors emerge in Deep Scene CNNs. arXiv: 1412.6856. Retrieved from https://arxiv.org/abs/1412.6856
[4]
A. Mahendran and A. Vedaldi. 2015. Understanding deep image representations by inverting them. In Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), June 7-12, 2015, Boston, MA, USA. IEEE, New Jersey, NJ, 5188-5196. https://doi.org/10.1109/CVPR.2015.7299155
[5]
Dosovitskiy A, Brox T. 2015. Inverting convolutional networks with convolutional networks. arXiv: 1506.02753. Retrieved from https://arxiv.org/abs/1506.02753
[6]
Guangli Wu, Zhenzhou Guo, Leiting Li, and Chengxiang Wang. 2021. Video abnormal event detection by fusing FCN and LSTM. Journal of Shanghai Jiaotong University 55, 05 (may 2021), 607-614.
[7]
Zequn Jie, Yunchao Wei, Xiaojie Jin, Jiashi Feng and Wei Liu. 2017. Deep Self-Taught Learning for Weakly Supervised Object Localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017CVPR), July 21-26, 2017, Honolulu, HI, USA. IEEE, New Jersey, NJ, 1377-1385. https://doi.org/10.1109/CVPR.2017.457
[8]
Loris Bazzani, Alessandra Bergamo, Dragomir Anguelov and Lorenzo Torresani. 2016. Self-taught Object Localization with Deep Networks. In Proceedings of 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA. IEEE, New Jersey, NJ, 1-9. https://doi.org/10.1109/WACV.2016.7477688
[9]
Cinbis R G, Verbeek J, Schmid C. 2017. Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 39, 1 (February 2016), 189-203.
[10]
Pedro O. Pinheiro and Ronan Collobert. 2015. From Image-level to Pixel-level Labeling with Convolutional Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015CVPR), June 7-12, 2015, Boston, MA, USA. IEEE, New Jersey, NJ, 1713-1721. https://doi.org/10.1109/CVPR.2015.7298780
[11]
Maxime Oquab, Leon Bottou, Ivan Laptev and Josef Sivic. 2014. Learning and transferring mid-level image representations using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014CVPR), June 23-28, 2014, Columbus, OH, USA. IEEE, New Jersey, NJ, 1717-1724. https://doi.org/10.1109/cvpr.2014.222
[12]
Maxime Oquab, Leon Bottou, Ivan Laptev and Josef Sivic. 2015. Is object localization for free? - Weakly-supervised learning with convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2015CVPR), June 7-12, 2015, Boston, MA, USA. IEEE, New Jersey, NJ, 685-694. https://doi.org/10.1109/cvpr.2015.7298668
[13]
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva and Antonio Torralba. 2016. Learning Deep Features for Discriminative Localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016CVPR), June 27-30, 2016, Las Vegas, NV, USA. IEEE, New Jersey, NJ, 2921-2929. https://doi.org/10.1109/cvpr.2016.319
[14]
Xiaolin Zhang, Yunchao Wei, Jiashi Feng, Yi Yang and Thomas S. Huang. 2018. Adversarial Complementary Learning for Weakly Supervised Object Localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018CVPR), June 18-23, 2018, Salt Lake City, UT, USA. IEEE, New Jersey, NJ, 1325-1334. https://doi.org/10.1109/cvpr.2018.00144
[15]
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh and Dhruv Batra. 2017. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017CVPR), July 21-26, 2017, Honolulu, HI, USA. IEEE, New Jersey, NJ, 618-626. https://doi.org/10.1109/iccv.2017.74
[16]
Gonzalez R C. 2018. Deep Convolutional Neural Networks [Lecture Notes]. IEEE Signal Processing Magazine. 35, 6 (November 2018), 79-87.
[17]
Sanghyun Woo, Jongchan Park, Joon-Young Lee and In So Kweon. 2018. CBAM: Convolutional Block Attention Module. arXiv: 1807.06521. Retrieved from https://arxiv.org/abs/1807.06521
[18]
Guangli Wu, Zhenzhou Guo, Leiting Li and Chengxiang Wang. 2020. Video Abnormal Event Detection Based on CNN and LSTM. In Proceedings of 2020 IEEE 5th International Conference on Signal and Image Processing (2020ICSIP), Nanjing, China. IEEE, New Jersey, NJ, 334-338. https://doi.org/10.1109/icsip49896.2020.9339428
[19]
Zhang Qin, Tian Yingjie, Liu Dalian. 2013. Nonparallel Support Vector Machines for Multiple-Instance Learning. Procedia Computer Science. 17, (December 2013), 1063-1072.
[20]
Guangli Wu, Zhenzhou Guo, Mianzhao Wang, Leiting Li and Chengxiang Wang. 2021. Video abnormal event detection based on CNN and multiple instance learning. In Proceedings of Twelfth International Conference on Signal Processing Systems (2021SPIE), Shanghai, China. SPIE, Bellingham WA, 1-6. https://doi.org/10.1117/12.2589031
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June 2021
95 pages
ISBN:9781450389419
DOI:10.1145/3474906
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Published: 06 October 2021
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ICGSP 2021
ICGSP 2021: 2021 the 5th International Conference on Graphics and Signal Processing
June 25 - 27, 2021
Nagoya, Japan
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