IBaggedFCNet: An Ensemble Framework for Anomaly Detection in Surveillance Videos

Zahid, Yumna; Tahir, Muhammad Atif; Durrani, Muhammad; Bouridane, Ahmed

doi:10.1109/access.2020.3042222

Cited by 13 publications

(8 citation statements)

References 39 publications

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“…However, from Table 5, it is hard to notice the best performative method as an individual method could not achieve an absolute better performance. For example, Mu et al [109], Cho et al [131], Xia et al [104], Zahid et al [87], and Roy et al [91] achieved the best AUC scores of 0.952, 0.992, 0.922, 0.940, and 0.997 from UCSD-Ped1 [31], UCSD-Ped2 [31], CUHK-Avenue [32], ShanghaiTech-Campus [18], and UMN [36], respectively. Unambiguously, considering experimental results in Table 5, it is very hard to find that one algorithm is better than its alternatives.…”

Section: Experimental Results Comparisonmentioning

confidence: 99%

Deep Crowd Anomaly Detection by Fusing Reconstruction and Prediction Networks

2023

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Abnormal event detection is one of the most challenging tasks in computer vision. Many existing deep anomaly detection models are based on reconstruction errors, where the training phase is performed using only videos of normal events and the model is then capable to estimate frame-level scores for an unknown input. It is assumed that the reconstruction error gap between frames of normal and abnormal scores is high for abnormal events during the testing phase. Yet, this assumption may not always hold due to superior capacity and generalization of deep neural networks. In this paper, we design a generalized framework (rpNet) for proposing a series of deep models by fusing several options of a reconstruction network (rNet) and a prediction network (pNet) to detect anomaly in videos efficiently. In the rNet, either a convolutional autoencoder (ConvAE) or a skip connected ConvAE (AEc) can be used, whereas in the pNet, either a traditional U-Net, a non-local block U-Net, or an attention block U-Net (aUnet) can be applied. The fusion of both rNet and pNet increases the error gap. Our deep models have distinct degree of feature extraction capabilities. One of our models (AEcaUnet) consists of an AEc with our proposed aUnet has capability to confirm better error gap and to extract high quality of features needed for video anomaly detection. Experimental results on UCSD-Ped1, UCSD-Ped2, CUHK-Avenue, ShanghaiTech-Campus, and UMN datasets with rigorous statistical analysis show the effectiveness of our models.

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Section: Experimental Results Comparisonmentioning

confidence: 99%

Deep Crowd Anomaly Detection by Fusing Reconstruction and Prediction Networks

2023

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“…The maximum F1-score achieved on the proposed was 98.8%. Al-khatib et al [78] presented a two-stage approach to identify anomalies in CT scans. At a pre-training stage, a convolutional neural network (CNN) was trained to learn a representation of the CT scan.…”

Section: Ct Scanmentioning

confidence: 99%

Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review

Nawaz,

Khan,

Ahmad

2024

IEEE Access

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In the context of biomedical data, an anomaly could refer to a rare or new type of disease, an aberration from normal behavior, or an unexpected observation requiring immediate attention. The detection of anomalies in biomedical data has a direct impact on the health and safety of individuals. However, anomalous events are rare, diverse, and infrequent. Often, the collection of anomalous data may involve significant loss of human life and healthcare costs. Therefore, traditional supervised machine and deep learning algorithms may not be directly applicable to such problems. Biomedical data are often collected in the form of images, electronic health records, and time series. Typically, an autoencoder (AE) or its corresponding variant is trained on normal data, and an anomaly is identified as a significant deviation from these data based on reconstruction error or other metrics. An Ensemble of AEs (EoAEs) can serve as a robust approach for anomaly detection in biomedical data by combining diverse and accurate views of normal data. An EoAE can provide superior detection to a single encoder; however, its performance can depend on various factors, including the diversity of the created data, the accuracy of the individual AEs, and the combination of their outcomes. Herein, we perform a comprehensive narrative literature review on the use of EoAEs when using different types of biomedical data. Such an ensemble provides a promising approach for anomaly detection in biomedical data, offering the potential for performance improvement by leveraging the strengths of diverse AEs. However, several challenges remain, such as the need for data specification and determination of the optimal number of AEs in the ensemble. By addressing these challenges, researchers can enhance the effectiveness of EoAEs for anomaly detection in various types of biomedical data. Furthermore, through this review, we highlight the significance of evaluating and comparing the performance of an EoAE with that of single AEs by establishing agreed-upon evaluation metrics and investigating normalization techniques for anomaly scores. We conclude the review by presenting challenges and open questions in the field with for future research.

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“…The abnormal cuboids input to the two streams ST CAE and calculate the abnormal score of appearance and motion based on reconstruction loss. Finally, the patches are classified into normal and abnormal events.The authors [20] presented IBaggedFCNet for video anomaly detection. Inception V3 is used for feature extraction.…”

Section: Classificationmentioning

confidence: 99%

A Review of Deep Learning Algorithms for Anomaly Detection in Videos

Sadatcharam,

Muruganadam

2023

IJSSE

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Anomaly detection in video assists in the resolution of a wide range of problems. A robust anomaly detection model is necessary due to the growing use of surveillance cameras in both indoor and outdoor settings. As a result, numerous strategies have been proposed in this field. Anomaly detection has already been the subject of several surveys. This survey focuses on deep learning approaches based on video anomaly detection. we categorize the various Deep Learning approaches according to their objectives like score based, future frame-based, Clasiification and reconstruction error based approaches. Additionally, it discusses evaluation criteria and commonly used datasets. We also suggest some possible directions future directions for research.

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IBaggedFCNet: An Ensemble Framework for Anomaly Detection in Surveillance Videos

Cited by 13 publications

References 39 publications

Deep Crowd Anomaly Detection by Fusing Reconstruction and Prediction Networks

Deep Crowd Anomaly Detection by Fusing Reconstruction and Prediction Networks

Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review

A Review of Deep Learning Algorithms for Anomaly Detection in Videos

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