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Networking Systems for Video Anomaly Detection: A Tutorial and Survey (2405.10347v2)

Published 16 May 2024 in cs.CV, cs.AI, and cs.CY

Abstract: The increasing utilization of surveillance cameras in smart cities, coupled with the surge of online video applications, has heightened concerns regarding public security and privacy protection, which propelled automated Video Anomaly Detection (VAD) into a fundamental research task within the AI community. With the advancements in deep learning and edge computing, VAD has made significant progress and advances synergized with emerging applications in smart cities and video internet, which has moved beyond the conventional research scope of algorithm engineering to deployable Networking Systems for VAD (NSVAD), a practical hotspot for intersection exploration in the AI, IoVT, and computing fields. In this article, we delineate the foundational assumptions, learning frameworks, and applicable scenarios of various deep learning-driven VAD routes, offering an exhaustive tutorial for novices in NSVAD. This article elucidates core concepts by reviewing recent advances and typical solutions and aggregating available research resources accessible at https://github.com/fdjingliu/NSVAD. Additionally, we showcase our latest NSVAD research in industrial IoT and smart cities, along with an end-cloud collaborative architecture for deployable NSVAD. Lastly, this article projects future development trends and discusses how the integration of AI and computing technologies can address existing research challenges and promote open opportunities, serving as an insightful guide for prospective researchers and engineers.

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References (165)
  1. UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 20143–20153.
  2. Robust real-time unusual event detection using multiple fixed-location monitors. IEEE transactions on pattern analysis and machine intelligence 30, 3 (2008), 555–560.
  3. Cross-domain video anomaly detection without target domain adaptation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2579–2591.
  4. Hierarchical scene normality-binding modeling for anomaly detection in surveillance videos. In Proceedings of the 30th ACM International Conference on Multimedia. 6103–6112.
  5. Suvramalya Basak and Anjali Gautam. 2024. Diffusion-based normality pre-training for weakly supervised video anomaly detection. Expert Systems with Applications (2024), 124013.
  6. Abnormal events detection based on spatio-temporal co-occurences. In 2009 IEEE conference on computer vision and pattern recognition. IEEE, 2458–2465.
  7. A review on outlier/anomaly detection in time series data. ACM Computing Surveys (CSUR) 54, 3 (2021), 1–33.
  8. Outlier detection: Methods, models, and classification. ACM Computing Surveys (CSUR) 53, 3 (2020), 1–37.
  9. Appearance-motion memory consistency network for video anomaly detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 938–946.
  10. Video anomaly detection with multi-scale feature and temporal information fusion. Neurocomputing 423 (2021), 264–273.
  11. A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 20392–20401.
  12. Context recovery and knowledge retrieval: A novel two-stream framework for video anomaly detection. IEEE Transactions on Image Processing (2024).
  13. Joao Carreira and Andrew Zisserman. 2017. Quo vadis, action recognition? a new model and the kinetics dataset. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 6299–6308.
  14. Anomaly detection in surveillance videos: a thematic taxonomy of deep models, review and performance analysis. Artificial Intelligence Review (2022), 1–50.
  15. Video anomaly detection with spatio-temporal dissociation. Pattern Recognition 122 (2021), 108213.
  16. Clustering driven deep autoencoder for video anomaly detection. In European Conference on Computer Vision. Springer, 329–345.
  17. Comprehensive Regularization in a Bi-directional Predictive Network for Video Anomaly Detection. In Proceedings of the American association for artificial intelligence. 1–9.
  18. Chang Wen Chen. 2020. Internet of video things: Next-generation IoT with visual sensors. IEEE Internet of Things Journal 7, 8 (2020), 6676–6685.
  19. Anomaly detection in surveillance video based on bidirectional prediction. Image and Vision Computing 98 (2020), 103915.
  20. NM-GAN: Noise-modulated generative adversarial network for video anomaly detection. Pattern Recognition 116 (2021), 107969.
  21. Learning graph enhanced spatial-temporal coherence for video anomaly detection. IEEE Signal Processing Letters 30 (2023), 314–318.
  22. Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2909–2917.
  23. Yong Shean Chong and Yong Haur Tay. 2017. Abnormal event detection in videos using spatiotemporal autoencoder. In International symposium on neural networks. Springer, 189–196.
  24. DeepAnomaly: Combining background subtraction and deep learning for detecting obstacles and anomalies in an agricultural field. Sensors 16, 11 (2016), 1904.
  25. Anomaly detection for IoT time-series data: A survey. IEEE Internet of Things Journal 7, 7 (2019), 6481–6494.
  26. Abnormal detection using interaction energy potentials. In CVPR 2011. IEEE, 3161–3167.
  27. Navneet Dalal and Bill Triggs. 2005. Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), Vol. 1. Ieee, 886–893.
  28. Human detection using oriented histograms of flow and appearance. In European conference on computer vision. Springer, 428–441.
  29. Residual spatiotemporal autoencoder for unsupervised video anomaly detection. Signal, Image and Video Processing 15, 1 (2021), 215–222.
  30. Behavior recognition via sparse spatio-temporal features. In 2005 IEEE international workshop on visual surveillance and performance evaluation of tracking and surveillance. IEEE, 65–72.
  31. Dual discriminator generative adversarial network for video anomaly detection. IEEE Access 8 (2020), 88170–88176.
  32. Keval Doshi and Yasin Yilmaz. 2021. Online anomaly detection in surveillance videos with asymptotic bound on false alarm rate. Pattern Recognition 114 (2021), 107865.
  33. Multimodal learning with graphs. Nature Machine Intelligence 5, 4 (2023), 340–350.
  34. Video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder. Computer Vision and Image Understanding 195 (2020), 102920.
  35. Mist: Multiple instance self-training framework for video anomaly detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 14009–14018.
  36. Convolutional transformer based dual discriminator generative adversarial networks for video anomaly detection. In Proceedings of the 29th ACM International Conference on Multimedia. 5546–5554.
  37. Recent advances in open set recognition: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 10 (2020), 3614–3631.
  38. Anomaly detection in video via self-supervised and multi-task learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 12742–12752.
  39. A background-agnostic framework with adversarial training for abnormal event detection in video. IEEE transactions on pattern analysis and machine intelligence 44, 9 (2021), 4505–4523.
  40. Ross Girshick. 2015. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision. 1440–1448.
  41. Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 1705–1714.
  42. Mutuality Attribute Makes Better Video Anomaly Detection. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2670–2674.
  43. Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey. arXiv preprint arXiv:2403.14608 (2024).
  44. Learning temporal regularity in video sequences. In Proceedings of the IEEE conference on computer vision and pattern recognition. 733–742.
  45. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 16000–16009.
  46. Multiple instance learning. Springer.
  47. Joint detection and recounting of abnormal events by learning deep generic knowledge. In Proceedings of the IEEE international conference on computer vision. 3619–3627.
  48. Self-supervised anomaly detection in computer vision and beyond: A survey and outlook. Neural Networks (2024), 106106.
  49. Detecting Anomalous Events from Unlabeled Videos via Temporal Masked Auto-Encoding. In 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1–6.
  50. Video anomaly detection using deep incremental slow feature analysis network. IET Computer Vision 10, 4 (2016), 258–267.
  51. Squirrel-cage local binary pattern and its application in video anomaly detection. IEEE Transactions on Information Forensics and Security 14, 4 (2018), 1007–1022.
  52. Abnormal event detection using deep contrastive learning for intelligent video surveillance system. IEEE Transactions on Industrial Informatics 18, 8 (2021), 5171–5179.
  53. Object-centric auto-encoders and dummy anomalies for abnormal event detection in video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7842–7851.
  54. Video caching, analytics, and delivery at the wireless edge: A survey and future directions. IEEE Communications Surveys & Tutorials 23, 1 (2020), 431–471.
  55. A survey on multi-access edge computing applied to video streaming: Some research issues and challenges. IEEE Communications Surveys & Tutorials 23, 2 (2021), 871–903.
  56. Longlong Jing and Yingli Tian. 2020. Self-supervised visual feature learning with deep neural networks: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 11 (2020), 4037–4058.
  57. An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos. Journal of Imaging 4, 2 (2018), 36.
  58. Kwang-Eun Ko and Kwee-Bo Sim. 2018. Deep convolutional framework for abnormal behavior detection in a smart surveillance system. Engineering Applications of Artificial Intelligence 67 (2018), 226–234.
  59. Yu Kong and Yun Fu. 2022. Human action recognition and prediction: A survey. International Journal of Computer Vision 130, 5 (2022), 1366–1401.
  60. Cross-modality integration framework with prediction, perception and discrimination for video anomaly detection. Neural Networks 172 (2024), 106138.
  61. Context-related video anomaly detection via generative adversarial network. Pattern Recognition Letters 156 (2022), 183–189.
  62. Spatial-temporal cascade autoencoder for video anomaly detection in crowded scenes. IEEE Transactions on Multimedia 23 (2020), 203–215.
  63. Two-stream deep spatial-temporal auto-encoder for surveillance video abnormal event detection. Neurocomputing 439 (2021), 256–270.
  64. Deep unsupervised anomaly detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 3636–3645.
  65. Anomaly detection and localization in crowded scenes. IEEE transactions on pattern analysis and machine intelligence 36, 1 (2013), 18–32.
  66. Spatio-Temporal Unity Networking for Video Anomaly Detection. IEEE Access 7 (2019), 172425–172432.
  67. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems (2021).
  68. A survey on explainable anomaly detection. ACM Transactions on Knowledge Discovery from Data 18, 1 (2023), 1–54.
  69. Future frame prediction for anomaly detection–a new baseline. In Proceedings of the IEEE conference on computer vision and pattern recognition. 6536–6545.
  70. Osin: Object-centric scene inference network for unsupervised video anomaly detection. IEEE Signal Processing Letters 30 (2023), 359–363.
  71. Memory-enhanced spatial-temporal encoding framework for industrial anomaly detection system. Expert Systems with Applications (2024), 123718. https://doi.org/10.1016/j.eswa.2024.123718
  72. Classifier Two Sample Test for Video Anomaly Detections.. In BMVC. 71.
  73. Learning Attention Augmented Spatial-temporal Normality for Video Anomaly Detection. In 2021 3rd International Symposium on Smart and Healthy Cities (ISHC). IEEE, 137–144.
  74. Appearance-Motion United Auto-Encoder Framework for Video Anomaly Detection. IEEE Transactions on Circuits and Systems II: Express Briefs 69, 5 (2022), 2498–2502.
  75. Abnormal Event Detection with Self-guiding Multi-instance Ranking Framework. In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 01–07.
  76. AMP-Net: Appearance-Motion Prototype Network Assisted Automatic Video Anomaly Detection System. IEEE Transactions on Industrial Informatics 20, 2 (2024), 2843–2855.
  77. Learning Appearance-Motion Normality for Video Anomaly Detection. In 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1–6.
  78. Learning Causality-inspired Representation Consistency for Video Anomaly Detection. In Proceedings of the 31st ACM International Conference on Multimedia. 203–212.
  79. Generalized video anomaly event detection: Systematic taxonomy and comparison of deep models. Comput. Surveys (2024).
  80. A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 13588–13597.
  81. Generating anomalies for video anomaly detection with prompt-based feature mapping. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 24500–24510.
  82. Abnormal event detection at 150 fps in matlab. In Proceedings of the IEEE international conference on computer vision. 2720–2727.
  83. Remembering history with convolutional lstm for anomaly detection. In 2017 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 439–444.
  84. Future frame prediction network for video anomaly detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).
  85. Video anomaly detection with sparse coding inspired deep neural networks. IEEE transactions on pattern analysis and machine intelligence 43, 3 (2019), 1070–1084.
  86. Anomaly detection in crowded scenes. In 2010 IEEE computer society conference on computer vision and pattern recognition. IEEE, 1975–1981.
  87. Deep anomaly detection on set data: Survey and comparison. Pattern Recognition (2024), 110381.
  88. Jefferson Ryan Medel and Andreas Savakis. 2016. Anomaly detection in video using predictive convolutional long short-term memory networks. arXiv preprint arXiv:1612.00390 (2016).
  89. Skeletal Video Anomaly Detection Using Deep Learning: Survey, Challenges, and Future Directions. IEEE Transactions on Emerging Topics in Computational Intelligence (2024).
  90. Abnormal image detection in endoscopy videos using a filter bank and local binary patterns. Neurocomputing 144 (2014), 70–91.
  91. Spatiotemporal anomaly detection using deep learning for real-time video surveillance. IEEE Transactions on Industrial Informatics 16, 1 (2019), 393–402.
  92. A comprehensive review on deep learning-based methods for video anomaly detection. Image and Vision Computing 106 (2021), 104078.
  93. Anomaly detection in traffic surveillance videos with gan-based future frame prediction. In Proceedings of the 2020 International Conference on Multimedia Retrieval. 457–463.
  94. Trong-Nguyen Nguyen and Jean Meunier. 2019. Anomaly detection in video sequence with appearance-motion correspondence. In Proceedings of the IEEE/CVF international conference on computer vision. 1273–1283.
  95. Memory-enhanced appearance-motion consistency framework for video anomaly detection. Computer Communications 216 (2024), 159–167.
  96. A review on deep learning techniques for video prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 6 (2020), 2806–2826.
  97. Deep learning for anomaly detection: A review. ACM Computing Surveys (CSUR) 54, 2 (2021), 1–38.
  98. Self-trained deep ordinal regression for end-to-end video anomaly detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 12173–12182.
  99. Learning memory-guided normality for anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14372–14381.
  100. Video anomaly detection guided by clustering learning. Pattern Recognition (2024), 110550.
  101. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 8748–8763.
  102. Analysis of anomaly detection in surveillance video: recent trends and future vision. Multimedia Tools and Applications (2022), 1–17.
  103. Bharathkumar Ramachandra and Michael Jones. 2020. Street Scene: A new dataset and evaluation protocol for video anomaly detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2569–2578.
  104. A survey of single-scene video anomaly detection. IEEE transactions on pattern analysis and machine intelligence 44, 5 (2020), 2293–2312.
  105. Plug-and-play cnn for crowd motion analysis: An application in abnormal event detection. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 1689–1698.
  106. Abnormal event detection in videos using generative adversarial nets. In 2017 IEEE international conference on image processing (ICIP). IEEE, 1577–1581.
  107. Training adversarial discriminators for cross-channel abnormal event detection in crowds. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 1896–1904.
  108. Dall-e: Creating images from text. UGC Care Group I Journal 8, 14 (2021), 71–75.
  109. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. 779–788.
  110. A survey on deep learning-based real-time crowd anomaly detection for secure distributed video surveillance. Personal and Ubiquitous Computing (2021), 1–17.
  111. Stream-51: Streaming classification and novelty detection from videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 228–229.
  112. Mehrsan Javan Roshtkhari and Martin D Levine. 2013. An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions. Computer vision and image understanding 117, 10 (2013), 1436–1452.
  113. Real-time anomaly detection and localization in crowded scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 56–62.
  114. Deep-cascade: Cascading 3d deep neural networks for fast anomaly detection and localization in crowded scenes. IEEE Transactions on Image Processing 26, 4 (2017), 1992–2004.
  115. Venkatesh Saligrama and Zhu Chen. 2012. Video anomaly detection based on local statistical aggregates. In 2012 IEEE Conference on computer vision and pattern recognition. IEEE, 2112–2119.
  116. Video anomaly identification. IEEE Signal Processing Magazine 27, 5 (2010), 18–33.
  117. Video Anomaly Detection: A Systematic Review of Issues and Prospects. Neurocomputing (2024), 127726.
  118. Durgesh Samariya and Amit Thakkar. 2023. A comprehensive survey of anomaly detection algorithms. Annals of Data Science 10, 3 (2023), 829–850.
  119. Anomaly detection in road traffic using visual surveillance: A survey. ACM Computing Surveys (CSUR) 53, 6 (2020), 1–26.
  120. Research on Nonlinear Systems Modeling Methods Based on Neural Networks. In Journal of Physics: Conference Series, Vol. 2095. IOP Publishing, 012037.
  121. Prakhar Singh and Vinod Pankajakshan. 2018. A Deep Learning Based Technique for Anomaly Detection in Surveillance Videos. In 2018 Twenty Fourth National Conference on Communications (NCC). 1–6.
  122. Deep appearance features for abnormal behavior detection in video. In International Conference on Image Analysis and Processing. Springer, 779–789.
  123. Networking systems of AI: on the convergence of computing and communications. IEEE Internet of Things Journal 9, 20 (2022), 20352–20381.
  124. Real-world anomaly detection in surveillance videos. In Proceedings of the IEEE conference on computer vision and pattern recognition. 6479–6488.
  125. Abnormal event detection for video surveillance using deep one-class learning. Multimedia Tools and Applications 78, 3 (2019), 3633–3647.
  126. Human action recognition from various data modalities: A review. IEEE transactions on pattern analysis and machine intelligence 45, 3 (2022), 3200–3225.
  127. Dual GroupGAN: An unsupervised Four-Competitor (2V2) approach for video anomaly detection. Pattern Recognition (2024), 110500.
  128. Latency-aware Road Anomaly Segmentation in Videos: A Photorealistic Dataset and New Metrics. arXiv preprint arXiv:2401.04942 (2024).
  129. Weakly-supervised video anomaly detection with robust temporal feature magnitude learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 4975–4986.
  130. Learning spatiotemporal features with 3d convolutional networks. In Proceedings of the IEEE international conference on computer vision. 4489–4497.
  131. Hanh TM Tran and David Hogg. 2017. Anomaly detection using a convolutional winner-take-all autoencoder. In Proceedings of the British Machine Vision Conference 2017. British Machine Vision Association.
  132. Convex polytope ensembles for spatio-temporal anomaly detection. In International Conference on Image Analysis and Processing. Springer, 174–184.
  133. Energy-based models for video anomaly detection. arXiv preprint arXiv:1708.05211 (2017).
  134. AI-empowered fog/edge resource management for IoT applications: A comprehensive review, research challenges and future perspectives. IEEE Communications Surveys & Tutorials (2023).
  135. Generative neural networks for anomaly detection in crowded scenes. IEEE Transactions on Information Forensics and Security 14, 5 (2018), 1390–1399.
  136. Robust unsupervised video anomaly detection by multipath frame prediction. IEEE transactions on neural networks and learning systems (2021).
  137. Predrnn: A recurrent neural network for spatiotemporal predictive learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 2 (2022), 2208–2225.
  138. Toward Video Anomaly Retrieval From Video Anomaly Detection: New Benchmarks and Model. IEEE Transactions on Image Processing 33 (2024), 2213–2225.
  139. A deep one-class neural network for anomalous event detection in complex scenes. IEEE transactions on neural networks and learning systems 31, 7 (2019), 2609–2622.
  140. Not only look, but also listen: Learning multimodal violence detection under weak supervision. In European conference on computer vision. Springer, 322–339.
  141. Weakly supervised audio-visual violence detection. IEEE Transactions on Multimedia (2022).
  142. Open-Vocabulary Video Anomaly Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  143. Vadclip: Adapting vision-language models for weakly supervised video anomaly detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 6074–6082.
  144. Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes. In 2010 IEEE computer society conference on computer vision and pattern recognition. IEEE, 2054–2060.
  145. Detecting anomalous events in videos by learning deep representations of appearance and motion. Computer Vision and Image Understanding 156 (2017), 117–127.
  146. Auto-fpn: Automatic network architecture adaptation for object detection beyond classification. In Proceedings of the IEEE/CVF international conference on computer vision. 6649–6658.
  147. Multimodal learning with transformers: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023).
  148. Abnormal event detection from videos using a two-stream recurrent variational autoencoder. IEEE Transactions on Cognitive and Developmental Systems 12, 1 (2018), 30–42.
  149. Generalized out-of-distribution detection: A survey. arXiv preprint arXiv:2110.11334 (2021).
  150. Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection. arXiv preprint arXiv:2404.08531 (2024).
  151. Learning Spatio-Temporal Relations with Multi-Scale Integrated Perception for Video Anomaly Detection. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 4020–4024.
  152. Anopcn: Video anomaly detection via deep predictive coding network. In Proceedings of the 27th ACM International Conference on Multimedia. 1805–1813.
  153. A Systematic Review of Human Activity Recognition Based On Mobile Devices: Overview, Progress and Trends. IEEE Communications Surveys & Tutorials (2024).
  154. Video Anomaly Detection via Visual Cloze Tests. IEEE Transactions on Information Forensics and Security (2023).
  155. Cloze test helps: Effective video anomaly detection via learning to complete video events. In Proceedings of the 28th ACM International Conference on Multimedia. 583–591.
  156. Abnormal event detection and localization via adversarial event prediction. IEEE Transactions on Neural Networks and Learning Systems (2021).
  157. Old is gold: Redefining the adversarially learned one-class classifier training paradigm. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14183–14193.
  158. Generative Cooperative Learning for Unsupervised Video Anomaly Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14744–14754.
  159. Surveillance video anomaly detection via non-local U-Net frame prediction. Multimedia Tools and Applications (2022), 1–16.
  160. Efficient video frame insertion and deletion detection based on inconsistency of correlations between local binary pattern coded frames. Security and Communication networks 8, 2 (2015), 311–320.
  161. Exploiting Spatial-temporal Correlations for Video Anomaly Detection. In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 1727–1733.
  162. Spatio-temporal autoencoder for video anomaly detection. In Proceedings of the 25th ACM international conference on Multimedia. 1933–1941.
  163. Anomalynet: An anomaly detection network for video surveillance. IEEE Transactions on Information Forensics and Security 14, 10 (2019), 2537–2550.
  164. Spatial–temporal convolutional neural networks for anomaly detection and localization in crowded scenes. Signal Processing: Image Communication 47 (2016), 358–368.
  165. Towards open set video anomaly detection. In European Conference on Computer Vision. Springer, 395–412.
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