Ensemble Learning Using Bagging And Inception-V3 For Anomaly Detection In Surveillance Videos

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

doi:10.1109/icip40778.2020.9190673

Cited by 12 publications

(7 citation statements)

References 14 publications

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“…Usually, majority voting or some other method of consolidation is used to reach a final decision. Bagging or Bootstrap aggregation is a technique in which several week base models are combined to form one strong predictive model [51]. Bagging technique is classified into two subtypes, Bootstrapping and Aggregation.…”

Section: Ensemble Learningmentioning

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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Section: Ensemble Learningmentioning

confidence: 99%

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

Nawaz,

Khan,

Ahmad

2024

IEEE Access

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“…These models have been well utilized in the image recognition arena. Zahid et al [16] utilized the Inception V3 model for anomaly detection in surveillance cameras using Spatial and temporal feature extraction. Li et al [17] DenseNet and Region Proposal Network models are used for object detection on PASCAL VOC and MS-COCO datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Deep Convolutional Neural Network Based Object Detection Model for Visually Challenged People

Devi¹,

Albraikan²,

Al‐Wesabi³

et al. 2023

Computer Systems Science and Engineering

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Artificial Intelligence (AI) and Computer Vision (CV) advancements have led to many useful methodologies in recent years, particularly to help visually-challenged people. Object detection includes a variety of challenges, for example, handling multiple class images, images that get augmented when captured by a camera and so on. The test images include all these variants as well. These detection models alert them about their surroundings when they want to walk independently. This study compares four CNN-based pre-trained models: Residual Network (ResNet-50), Inception v3, Dense Convolutional Network (DenseNet-121), and SqueezeNet, predominantly used in image recognition applications. Based on the analysis performed on these test images, the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and speed. To further improve the performance of the Inception v3 model, the thermal exchange optimization (TEO) algorithm is applied to tune the hyperparameters (number of epochs, batch size, and learning rate) showing the novelty of the work. Better accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer, hyperparameter optimizer, and factorization approach. Additionally, Inception V3 can handle images of different sizes. This makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things (IoT)-based devices.

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“…Pretrained models were used to extract features from labelled video and imagery data. The pre-trained models used mostly in the reviewed models include deep 3-dimensional convolutional networks (C3D) Model [2], Inception V3 Module [15], I3D, You Look Only Once Version 3 (YOLOV3). C3D borrows from BVLC Caffee which was modified to support 3D Convolution and pooling [16].…”

Section: A Transfer Learningmentioning

confidence: 99%

“…Transfer learning was identified in the following papers, Sultani [2] [20], [21], [22], [23], [15], [24]. In this review, 30% out of the 30 papers reviewed had adopted the transfer learning paradigm by using pre-trained models to improve the performance of the new models.…”

Section: A Transfer Learningmentioning

confidence: 99%

“…Other reviewed models are random cases of ensemble learning that combines multiple learning algorithms to get better predictive performance than the constituent learning algorithms alone. For instance, Zahid and others [15] is a typical case of ensemble and transfer learning. The model combines both a 3D convolutional network and a Fully Connected (FC) Network.…”

Section: Ensemble Learningmentioning

confidence: 99%

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A Survey of Deep Learning Solutions for Anomaly Detection in Surveillance Videos

Munyua

Wambugu

Njenga

2021

IJCIT

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Deep learning has proven to be a landmark computing approach to the computer vision domain. Hence, it has been widely applied to solve complex cognitive tasks like the detection of anomalies in surveillance videos. Anomaly detection in this case is the identification of abnormal events in the surveillance videos which can be deemed as security incidents or threats. Deep learning solutions for anomaly detection has outperformed other traditional machine learning solutions. This review attempts to provide holistic benchmarking of the published deep learning solutions for videos anomaly detection since 2016. The paper identifies, the learning technique, datasets used and the overall model accuracy. Reviewed papers were organised into five deep learning methods namely; autoencoders, continual learning, transfer learning, reinforcement learning and ensemble learning. Current and emerging trends are discussed as well.

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Ensemble Learning Using Bagging And Inception-V3 For Anomaly Detection In Surveillance Videos

Cited by 12 publications

References 14 publications

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

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

Intelligent Deep Convolutional Neural Network Based Object Detection Model for Visually Challenged People

A Survey of Deep Learning Solutions for Anomaly Detection in Surveillance Videos

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