Hierarchical detection of abnormal behaviors in video surveillance through modeling normal behaviors based on AUC maximization

Feizi, A.

doi:10.1007/s00500-019-04544-9

Cited by 10 publications

(4 citation statements)

References 33 publications

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“…Meanwhile, many researchers provide theoretical guarantees for AUC optimization algorithms from different aspects, such as generalization analysis (Agarwal et al, 2005;Clémenc ¸on et al, 2008;Usunier et al, 2005) and consistency analysis (Agarwal, 2014;Gao & Zhou, 2015). Beyond the optimization algorithms and theoretical supports for AUC, in practice, AUC optimization demonstrates its effectiveness in various class-imbalanced tasks, such as disease prediction (Westcott et al, 2019;Gola et al, 2020;Ren et al, 2018), rare event detection (Feizi, 2020;Robles et al, 2020) and etc.…”

Section: Auc Optimizationmentioning

confidence: 99%

AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

Hou¹,

Xu²,

Zhang³

et al. 2022

Preprint

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Section: Auc Optimizationmentioning

confidence: 99%

AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

Hou¹,

Xu²,

Zhang³

et al. 2022

Preprint

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“…A method based on maximization of the area under the ROC [4]. This method does not need manual labelling for estimation of the model.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep Learning Technique with One Class Svm for Anomaly Detection in Crowded Environment

2022

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Nowadays, the analysis of abnormal events becomes more and more exhausting due to the divine use of surveillance cameras. Reliability of normal and abnormal samples is generally different in practice. In the existing work, developed machine learning and swarm intelligence based approaches for anomaly detection by extracting spatiotemporal features from video sequences. This proposed abnormal event detection mechanism is obvious, but the tracking result is less precise. Some of the reasons are low quality video, system noise, small object, and other factors. In order to improve the precisions of the tracked object, this research work proposed a new hybrid deep learning and robust segmentation method for better and faster tracking result. In the proposed approach, a video frame is subjected to a series of operations to extract most salient information from it. Initially, a 2D variance plane is constructed to encode local spatio-temporal variations around each pixel in a video frame. The Improved Particle Swarm Optimization algorithm is then applied to isolate the most salient regions based on motion information in the 2D variance plane. Grey Level Co-occurrence Matrix is applied to the extracted salient pixels in the video. And then the segmentation process is done with the help of fuzzy c-means clustering of successive frames are exploited for pattern matching in a simple feature space. Finally developed a hybrid deep learning based on a pre-trained Convolution Neural Network and One-class SVM is trained with spatial features for robust classification of abnormal shapes. Thus the experimental result suggests that the proposed anomaly detection techniques outperform the existing techniques in context of accuracy and time complexity.

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“…Fourth, different theoretical guarantees have been examined, e.g., consistency [45], generalization error bounds [84], excess risk bounds [54,170], regret bounds [178], convergence rates or sample complexities [89], stability [85,163]. Last but not least, AUC maximization has been successfully investigated in a variety of applications [7,10,40,57,69,132,135,146,147,156,181,184], e.g., medical image classification [173] and molecular properties prediction [151], to mention but a few.…”

Section: Introductionmentioning

confidence: 99%

AUC Maximization in the Era of Big Data and AI: A Survey

Yang¹,

Ying²

2022

Preprint

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Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessing the performance of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that learns a predictive model by directly maximizing its AUC score. It has been studied for more than two decades dating back to late 90s and a huge amount of work has been devoted to AUC maximization since then. Recently, stochastic AUC maximization for big data and deep AUC maximization for deep learning have received increasing attention and yielded great impact for solving real-world problems. However, to the best our knowledge there is no comprehensive survey of related works for AUC maximization. This paper aims to address the gap by reviewing the literature in the past two decades. We not only give a holistic view of the literature but also present detailed explanations and comparisons of different papers from formulations to algorithms and theoretical guarantees. We also identify and discuss remaining and emerging issues for deep AUC maximization, and provide suggestions on topics for future work.

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Hierarchical detection of abnormal behaviors in video surveillance through modeling normal behaviors based on AUC maximization

Cited by 10 publications

References 33 publications

AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

Hybrid Deep Learning Technique with One Class Svm for Anomaly Detection in Crowded Environment

AUC Maximization in the Era of Big Data and AI: A Survey

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