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Motivated by our observation that motion information is the key to good anomaly detection performance in video, we propose a temporal augmented network to learn a motion-aware feature.
Abnormal Event Detection at 150 FPS in MATLAB
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Action Recognition with Improved Trajectories
Heng Wang and Cordelia Schmid · 2013
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Anomaly Detection and Localization in Crowded Scenes
Weixin Li, Vijay Mahadevan, and Nuno Vasconcelos · 2014
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Two-Stream Convolutional Networks for Action Recognition in Videos
Karen Simonyan and Andrew Zisserman · 2014
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A Duality Based Approach for Realtime TV-L1 Optical Flow
Christopher Zach, Thomas Pock, and Horst Bischof · 2014
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Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation
Christian Bailer, Bertram Taetz, and Didier Stricker · 2015
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Video Anomaly Detection and Localization Using Hierarchical Feature Representation and Gaussian Process Regression
Kai-Wen Cheng, Yie-Tarng Chen, and Wen-Hsien Fang · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2015
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Going Deeper with Convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Learning Spatiotemporal Features with 3D Convolutional Networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 2015
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Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
Dan Xu, Elisa Ricci, Yan Yan, Jingkuan Song, and Nicu Sebe · 2015
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Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-Encoders
Huan Yang, Baoyuan Wang, Stephen Lin, David Wipf, Minyi Guo, and Baining Guo · 2015
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Learning Temporal Regularity in Video Sequences
Mahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K. Roy-Chowdhury, and Larry S. Davis · 2016
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Novel Dataset for Fine-Grained Abnormal Behavior Understanding in Crowd
Hamidreza Rabiee, Javad Haddadnia, Hossein Mousavi, Maziyar Kalantarzadeh, Moin Nabi, and Vittorio Murino · 2016
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FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
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Long-term Temporal Convolutions for Action Recognition
Gul Varol, Ivan Laptev, and Cordelia Schmid · 2017
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Deepvs: A deep learning based video saliency prediction approach
Lai Jiang, Mai Xu, Tie Liu, Minglang Qiao, and Zulin Wang · 2018
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Dynamic Video Anomaly Detection and Localization Using Sparse Denoising Autoencoders
Medhini G. Narasimhan and Sowmya Kamath S · 2018
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Real-World Anomaly Detection in Surveillance Videos
Waqas Sultani, Chen Chen, and Mubarak Shah · 2018
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PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2018
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Jing Shao, Chen Change Loy, Kai Kang, and Xiaogang Wang · 2016
Cited alongside, same era.
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, and Luc Van Gool · 2016
Cited alongside, same era.
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Joao Carreira and Andrew Zisserman · 2017
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Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos
Rui Hou, Chen Chen, and Mubarak Shah · 2017
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Large-Scale Visual Relationship Understanding
Ji Zhang, Yannis Kalantidis, Marcus Rohrbach, Manohar Paluri, Ahmed Elgammal, and Mohamed Elhoseiny
Cited in the paper.
Graphical Contrastive Losses for Scene Graph Parsing
Ji Zhang, Kevin J. Shih, Ahmed Elgammal, Andrew Tao, and Bryan Catanzaro
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Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, and Kevin Murphy · 2018
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Towards Universal Representation for Unseen Action Recognition
Yi Zhu, Yang Long, Yu Guan, Shawn Newsam, and Ling Shao · 2018
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