2022
Ristea, Nicolae-Catalin; Madan, Neelu; Ionescu, Radu Tudor; Nasrollahi, Kamal; Khan, Fahad Shahbaz; Moeslund, Thomas B.; Shah, Mubarak
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection Conference
IEEE Computer Vision and Pattern Recognition, 2022.
Tags: Anomaly Detection, CVPR, Self-Supervised Learning, Self-Supervision | Links:
@conference{nokey_101,
title = {Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection},
author = {Nicolae-Catalin Ristea and Neelu Madan and Radu Tudor Ionescu and Kamal Nasrollahi and Fahad Shahbaz Khan and Thomas B. Moeslund and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/SSPCAB_camera-arxiv.pdf},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {Anomaly Detection, CVPR, Self-Supervised Learning, Self-Supervision},
pubstate = {published},
tppubtype = {conference}
}
Acsintoae, Andra; Florescu, Andrei; Georgescu, Mariana-Iuliana; Mare, Tudor; Sumedrea, Paul; Ionescu, Radu Tudor; Khan, Fahad Shahbaz; Shah, Mubarak
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection Conference
IEEE Computer Vision and Pattern Recognition, 2022.
Tags: Anomaly Detection, CVPR, Dataset | Links:
@conference{nokey_103,
title = {UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection},
author = {Andra Acsintoae and Andrei Florescu and Mariana-Iuliana Georgescu and Tudor Mare and Paul Sumedrea and Radu Tudor Ionescu and Fahad Shahbaz Khan and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/04315.pdf
https://github.com/lilygeorgescu/UBnormal},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {Anomaly Detection, CVPR, Dataset},
pubstate = {published},
tppubtype = {conference}
}
Ristea, Nicolae-Catalin; Madan, Neelu; Ionescu, Radu Tudor; Nasrollahi, Kamal; Khan, Fahad Shahbaz; Moeslund, Thomas B.; Shah, Mubarak
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection Conference
IEEE Computer Vision and Pattern Recognition, 2022.
Tags: Anomaly Detection, CVPR, Self-Supervised Learning, Self-Supervision | Links:
@conference{nokey_149,
title = {Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection},
author = {Nicolae-Catalin Ristea and Neelu Madan and Radu Tudor Ionescu and Kamal Nasrollahi and Fahad Shahbaz Khan and Thomas B. Moeslund and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/SSPCAB_camera-arxiv.pdf},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {Anomaly Detection, CVPR, Self-Supervised Learning, Self-Supervision},
pubstate = {published},
tppubtype = {conference}
}
Acsintoae, Andra; Florescu, Andrei; Georgescu, Mariana-Iuliana; Mare, Tudor; Sumedrea, Paul; Ionescu, Radu Tudor; Khan, Fahad Shahbaz; Shah, Mubarak
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection Conference
IEEE Computer Vision and Pattern Recognition, 2022.
Tags: Anomaly Detection, CVPR, Dataset | Links:
@conference{nokey_151,
title = {UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection},
author = {Andra Acsintoae and Andrei Florescu and Mariana-Iuliana Georgescu and Tudor Mare and Paul Sumedrea and Radu Tudor Ionescu and Fahad Shahbaz Khan and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/04315.pdf
https://github.com/lilygeorgescu/UBnormal},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {Anomaly Detection, CVPR, Dataset},
pubstate = {published},
tppubtype = {conference}
}
2021
Georgescu, Mariana Iuliana; Bărbălău, Antonio; Ionescu, Radu Tudor; Khan, Fahad Shahbaz; Popescu, Marius; Shah, Mubarak
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning Conference
IEEE Conference on Computer Vision and Pattern Recognition, 2021.
Abstract | Tags: Anomaly Detection, Multi-Task Learning, Self-Supervised Learning | Links:
@conference{georgescu2020anomaly,
title = {Anomaly Detection in Video via Self-Supervised and Multi-Task Learning},
author = {Mariana Iuliana Georgescu and Antonio Bărbălău and Radu Tudor Ionescu and Fahad Shahbaz Khan and Marius Popescu and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Publications_Anomaly-Detection-in-Video-via-Self-Supervised-and-Multi-Task-Learning.pdf
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Publications_Anomaly-Detection-in-Video-via-Self-Supervised-and-Multi-Task-Learning_Supp.pdf
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Anomaly-Detection-in-Video-via-Self-Supervised-and-Multi-Task-Learning.pptx
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Anomaly-Detection-in-Video-via-Self-Supervised-and-Multi-Task-Learning.mp4},
year = {2021},
date = {2021-06-19},
urldate = {2021-06-19},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition},
abstract = {Anomaly detection in video is a challenging computer vision problem. Due to the lack of anomalous events at training time, anomaly detection requires the design of learning methods without full supervision. In this paper, we approach anomalous event detection in video through self-supervised and multi-task learning at the object level. We first utilize a pre-trained detector to detect objects. Then, we train a 3D convolutional neural network to produce discriminative anomaly-specific information by jointly learning multiple proxy tasks: three self-supervised and one based on knowledge distillation. The self-supervised tasks are: (i) discrimination of forward/backward moving objects (arrow of time), (ii) discrimination of objects in consecutive/intermittent frames (motion irregularity) and (iii) reconstruction of object-specific appearance information. The knowledge distillation task takes into account both classification and detection information, generating large prediction discrepancies between teacher and student models when anomalies occur. To the best of our knowledge, we are the first to approach anomalous event detection in video as a multi-task learning problem, integrating multiple self-supervised and knowledge distillation proxy tasks in a single architecture. Our lightweight architecture outperforms the state-of-the-art methods on three benchmarks: Avenue, ShanghaiTech and UCSD Ped2. Additionally, we perform an ablation study demonstrating the importance of integrating self-supervised learning and normality-specific distillation in a multi-task learning setting.},
keywords = {Anomaly Detection, Multi-Task Learning, Self-Supervised Learning},
pubstate = {published},
tppubtype = {conference}
}
Georgescu, Mariana Iuliana; Bărbălău, Antonio; Ionescu, Radu Tudor; Khan, Fahad Shahbaz; Popescu, Marius; Shah, Mubarak
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning Conference
IEEE Conference on Computer Vision and Pattern Recognition, 2021.
Abstract | Tags: Anomaly Detection, Multi-Task Learning, Self-Supervised Learning | Links:
@conference{georgescu2020anomalyb,
title = {Anomaly Detection in Video via Self-Supervised and Multi-Task Learning},
author = {Mariana Iuliana Georgescu and Antonio Bărbălău and Radu Tudor Ionescu and Fahad Shahbaz Khan and Marius Popescu and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Publications_Anomaly-Detection-in-Video-via-Self-Supervised-and-Multi-Task-Learning.pdf
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Publications_Anomaly-Detection-in-Video-via-Self-Supervised-and-Multi-Task-Learning_Supp.pdf
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Anomaly-Detection-in-Video-via-Self-Supervised-and-Multi-Task-Learning.pptx
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Anomaly-Detection-in-Video-via-Self-Supervised-and-Multi-Task-Learning.mp4},
year = {2021},
date = {2021-06-19},
urldate = {2021-06-19},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition},
abstract = {Anomaly detection in video is a challenging computer vision problem. Due to the lack of anomalous events at training time, anomaly detection requires the design of learning methods without full supervision. In this paper, we approach anomalous event detection in video through self-supervised and multi-task learning at the object level. We first utilize a pre-trained detector to detect objects. Then, we train a 3D convolutional neural network to produce discriminative anomaly-specific information by jointly learning multiple proxy tasks: three self-supervised and one based on knowledge distillation. The self-supervised tasks are: (i) discrimination of forward/backward moving objects (arrow of time), (ii) discrimination of objects in consecutive/intermittent frames (motion irregularity) and (iii) reconstruction of object-specific appearance information. The knowledge distillation task takes into account both classification and detection information, generating large prediction discrepancies between teacher and student models when anomalies occur. To the best of our knowledge, we are the first to approach anomalous event detection in video as a multi-task learning problem, integrating multiple self-supervised and knowledge distillation proxy tasks in a single architecture. Our lightweight architecture outperforms the state-of-the-art methods on three benchmarks: Avenue, ShanghaiTech and UCSD Ped2. Additionally, we perform an ablation study demonstrating the importance of integrating self-supervised learning and normality-specific distillation in a multi-task learning setting.},
keywords = {Anomaly Detection, Multi-Task Learning, Self-Supervised Learning},
pubstate = {published},
tppubtype = {conference}
}
Georgescu, Mariana-Iuliana; Ionescu, Radu Tudor; Khan, Fahad Shahbaz; Popescu, Marius; Shah, Mubarak
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
Tags: Abnormal Event Detection, Anomaly Detection | Links:
@article{Georgescu2021,
title = {A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video},
author = {Mariana-Iuliana Georgescu and Radu Tudor Ionescu and Fahad Shahbaz Khan and Marius Popescu and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/AED_PAMI-revised-arxiv.pdf},
year = {2021},
date = {2021-04-16},
urldate = {2021-04-16},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Abnormal Event Detection, Anomaly Detection},
pubstate = {published},
tppubtype = {article}
}
2020
Georgescu, Mariana-Iuliana; Barbalau, Antonio; Ionescu, Radu Tudor; Khan, Fahad Shahbaz; Popescu, Marius; Shah, Mubarak
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning Technical Report
no. arXiv:2011.07491, 2020.
Tags: Anomaly Detection, CVPR-2021, Multi-Task Learning, Self-Supervised Learning | Links:
@techreport{Georgescu2020b,
title = {Anomaly Detection in Video via Self-Supervised and Multi-Task Learning},
author = {Mariana-Iuliana Georgescu and Antonio Barbalau and Radu Tudor Ionescu and Fahad Shahbaz Khan and Marius Popescu and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Publicatons_Anomaly-Detection-in-Video-via-Self-Supervised-and-Multi-Task-Learning.pdf},
year = {2020},
date = {2020-11-15},
urldate = {2020-11-15},
number = {arXiv:2011.07491},
howpublished = {CVPR-2021},
keywords = {Anomaly Detection, CVPR-2021, Multi-Task Learning, Self-Supervised Learning},
pubstate = {published},
tppubtype = {techreport}
}
Georgescu, Mariana-Iuliana; Barbalau, Antonio; Ionescu, Radu Tudor; Khan, Fahad Shahbaz; Popescu, Marius; Shah, Mubarak
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning Technical Report
no. arXiv:2011.07491, 2020.
Tags: Anomaly Detection, CVPR-2021, Multi-Task Learning, Self-Supervised Learning | Links:
@techreport{Georgescu2020bb,
title = {Anomaly Detection in Video via Self-Supervised and Multi-Task Learning},
author = {Mariana-Iuliana Georgescu and Antonio Barbalau and Radu Tudor Ionescu and Fahad Shahbaz Khan and Marius Popescu and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Publicatons_Anomaly-Detection-in-Video-via-Self-Supervised-and-Multi-Task-Learning.pdf},
year = {2020},
date = {2020-11-15},
urldate = {2020-11-15},
number = {arXiv:2011.07491},
howpublished = {CVPR-2021},
keywords = {Anomaly Detection, CVPR-2021, Multi-Task Learning, Self-Supervised Learning},
pubstate = {published},
tppubtype = {techreport}
}
Georgescu, Mariana-Iuliana; Ionescu, Radu Tudor; Khan, Fahad Shahbaz; Popescu, Marius; Shah, Mubarak
A Scene-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video Technical Report
no. arXiv:2008.12328, 2020.
Tags: Anomaly Detection | Links:
@techreport{Georgescu2020,
title = {A Scene-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video},
author = {Mariana-Iuliana Georgescu and Radu Tudor Ionescu and Fahad Shahbaz Khan and Marius Popescu and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2020/08/Publications_A-Scene-Agnostic-Framework-with-Adversarial-Training-for-Abnormal-Event-Detection-in-Video.pdf},
year = {2020},
date = {2020-08-27},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 8},
number = {arXiv:2008.12328},
keywords = {Anomaly Detection},
pubstate = {published},
tppubtype = {techreport}
}
Georgescu, Mariana-Iuliana; Ionescu, Radu Tudor; Khan, Fahad Shahbaz; Popescu, Marius; Shah, Mubarak
A Scene-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video Technical Report
no. arXiv:2008.12328, 2020.
Tags: Anomaly Detection | Links:
@techreport{Georgescu2020f,
title = {A Scene-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video},
author = {Mariana-Iuliana Georgescu and Radu Tudor Ionescu and Fahad Shahbaz Khan and Marius Popescu and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2020/08/Publications_A-Scene-Agnostic-Framework-with-Adversarial-Training-for-Abnormal-Event-Detection-in-Video.pdf},
year = {2020},
date = {2020-08-27},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 8},
number = {arXiv:2008.12328},
keywords = {Anomaly Detection},
pubstate = {published},
tppubtype = {techreport}
}
2018
Sultani, Waqas; Chen, Chen; Shah, Mubarak
Real-world Anomaly Detection in Surveillance Videos Conference
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, June 18-22, 2018., 2018.
Tags: Anomaly Detection | Links:
@conference{Sultani2018,
title = {Real-world Anomaly Detection in Surveillance Videos},
author = {Waqas Sultani and Chen Chen and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/anomaly_detection.pdf},
year = {2018},
date = {2018-04-03},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, June 18-22, 2018.},
keywords = {Anomaly Detection},
pubstate = {published},
tppubtype = {conference}
}
Sultani, Waqas; Chen, Chen; Shah, Mubarak
Real-world Anomaly Detection in Surveillance Videos Conference
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, June 18-22, 2018., 2018.
Tags: Anomaly Detection | Links:
@conference{Sultani2018b,
title = {Real-world Anomaly Detection in Surveillance Videos},
author = {Waqas Sultani and Chen Chen and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/anomaly_detection.pdf},
year = {2018},
date = {2018-04-03},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, June 18-22, 2018.},
keywords = {Anomaly Detection},
pubstate = {published},
tppubtype = {conference}
}
2010
Wu, Shandong; Moore, Brian; Shah, Mubarak
Chaotic Invariants of Lagrangian Particle Trajectories for Anomaly Detection in Crowded Scenes Conference
IEEE Conference on Computer Vision and Pattern Recognition, 2010.
Tags: Anomaly Detection, Crowd Analysis, Deep Learning | Links:
@conference{Wu2010,
title = {Chaotic Invariants of Lagrangian Particle Trajectories for Anomaly Detection in Crowded Scenes},
author = {Shandong Wu and Brian Moore and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/cvpr2010/1.pdf},
year = {2010},
date = {2010-12-06},
urldate = {2010-12-06},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition},
keywords = {Anomaly Detection, Crowd Analysis, Deep Learning},
pubstate = {published},
tppubtype = {conference}
}
Wu, Shandong; Moore, Brian; Shah, Mubarak
Chaotic Invariants of Lagrangian Particle Trajectories for Anomaly Detection in Crowded Scenes Conference
IEEE Conference on Computer Vision and Pattern Recognition, 2010.
Tags: Anomaly Detection, Crowd Analysis, Deep Learning | Links:
@conference{Wu2010b,
title = {Chaotic Invariants of Lagrangian Particle Trajectories for Anomaly Detection in Crowded Scenes},
author = {Shandong Wu and Brian Moore and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/cvpr2010/1.pdf},
year = {2010},
date = {2010-12-06},
urldate = {2010-12-06},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition},
keywords = {Anomaly Detection, Crowd Analysis, Deep Learning},
pubstate = {published},
tppubtype = {conference}
}
2009
Saleemi, Imran; Shafique, Khurram; Shah, Mubarak
Probabilistic Modeling of Scene Dynamics for Applications in VIsual Surveillance Journal Article
In: IEEE TPAMI, 2009.
Tags: Abnormal Event Detection, Anomaly Detection, Deep Learning, Motion Patterns | Links:
@article{Saleemi2009,
title = {Probabilistic Modeling of Scene Dynamics for Applications in VIsual Surveillance},
author = {Imran Saleemi and Khurram Shafique and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/TPAMI_scene_dynamics.pdf},
year = {2009},
date = {2009-01-29},
urldate = {2009-01-29},
journal = {IEEE TPAMI},
keywords = {Abnormal Event Detection, Anomaly Detection, Deep Learning, Motion Patterns},
pubstate = {published},
tppubtype = {article}
}
Saleemi, Imran; Shafique, Khurram; Shah, Mubarak
Probabilistic Modeling of Scene Dynamics for Applications in VIsual Surveillance Journal Article
In: IEEE TPAMI, 2009.
Tags: Abnormal Event Detection, Anomaly Detection, Deep Learning, Motion Patterns | Links:
@article{Saleemi2009b,
title = {Probabilistic Modeling of Scene Dynamics for Applications in VIsual Surveillance},
author = {Imran Saleemi and Khurram Shafique and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/TPAMI_scene_dynamics.pdf},
year = {2009},
date = {2009-01-29},
urldate = {2009-01-29},
journal = {IEEE TPAMI},
keywords = {Abnormal Event Detection, Anomaly Detection, Deep Learning, Motion Patterns},
pubstate = {published},
tppubtype = {article}
}
2008
Basharat, Arslan; Gritai, Alexei; Shah, Mubarak
Learning Object Motion Patterns for Anomaly Detection and Improved Object Detection Conference
IEEE Conference on Computer Vision and Pattern Recognition, 2008.
Tags: Anomaly Detection, Deep Learning, Life Patterns, Motion Patterns | Links:
@conference{Basharat2008,
title = {Learning Object Motion Patterns for Anomaly Detection and Improved Object Detection},
author = {Arslan Basharat and Alexei Gritai and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2020/04/Learning-Object-Motion-Patterns-for-Anomaly-Detection-and-Improved-Object-Detection.pdf},
year = {2008},
date = {2008-11-10},
urldate = {2008-11-10},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition},
keywords = {Anomaly Detection, Deep Learning, Life Patterns, Motion Patterns},
pubstate = {published},
tppubtype = {conference}
}
Basharat, Arslan; Gritai, Alexei; Shah, Mubarak
Learning Object Motion Patterns for Anomaly Detection and Improved Object Detection Conference
IEEE Conference on Computer Vision and Pattern Recognition, 2008.
Tags: Anomaly Detection, Deep Learning, Life Patterns, Motion Patterns | Links:
@conference{Basharat2008c,
title = {Learning Object Motion Patterns for Anomaly Detection and Improved Object Detection},
author = {Arslan Basharat and Alexei Gritai and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2020/04/Learning-Object-Motion-Patterns-for-Anomaly-Detection-and-Improved-Object-Detection.pdf},
year = {2008},
date = {2008-11-10},
urldate = {2008-11-10},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition},
keywords = {Anomaly Detection, Deep Learning, Life Patterns, Motion Patterns},
pubstate = {published},
tppubtype = {conference}
}