LightAnomalyNet: A Lightweight Framework for Efficient Abnormal Behavior Detection

Mehmood, Abid

doi:10.3390/s21248501

Cited by 11 publications

(8 citation statements)

References 71 publications

(102 reference statements)

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“…On the other hand, studies have shown that lowering the number of convolution layers has a significant impact on network performance; see, for example, [45,46]. Furthermore, limiting the number of convolution layers also has a significant impact on network performance in terms of computation efficiency [47,48]. Therefore, inspired by previous works [43,47], this study explored the relationship between network depth and its performance specifically using the HAM10000 dataset.…”

Section: Shortening the Architecturementioning

confidence: 98%

SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions

Mehmood

Gulzar

Ilyas

et al. 2023

Cancers

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Skin cancer is a major public health concern around the world. Skin cancer identification is critical for effective treatment and improved results. Deep learning models have shown considerable promise in assisting dermatologists in skin cancer diagnosis. This study proposes SBXception: a shallower and broader variant of the Xception network. It uses Xception as the base model for skin cancer classification and increases its performance by reducing the depth and expanding the breadth of the architecture. We used the HAM10000 dataset, which contains 10,015 dermatoscopic images of skin lesions classified into seven categories, for training and testing the proposed model. Using the HAM10000 dataset, we fine-tuned the new model and reached an accuracy of 96.97% on a holdout test set. SBXception also achieved significant performance enhancement with 54.27% fewer training parameters and reduced training time compared to the base model. Our findings show that reducing and expanding the Xception model architecture can greatly improve its performance in skin cancer categorization.

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Section: Shortening the Architecturementioning

confidence: 98%

SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions

Mehmood

Gulzar

Ilyas

et al. 2023

Cancers

Self Cite

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“…So, it is important to propose lightweight models that can work well with limited resource devices like mobile devices. Some possible solutions have been proposed by [100][101][102]. 9.…”

Section: Lightweight Modelsmentioning

confidence: 99%

Review on Deep Learning Approaches for Anomaly Event Detection in Video Surveillance

et al. 2022

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In the last few years, due to the continuous advancement of technology, human behavior detection and recognition have become important scientific research in the field of computer vision (CV). However, one of the most challenging problems in CV is anomaly detection (AD) because of the complex environment and the difficulty in extracting a particular feature that correlates with a particular event. As the number of cameras monitoring a given area increases, it will become vital to have systems capable of learning from the vast amounts of available data to identify any potential suspicious behavior. Then, the introduction of deep learning (DL) has brought new development directions for AD. In particular, DL models such as convolution neural networks (CNNs) and recurrent neural networks (RNNs) have achieved excellent performance dealing with AD tasks, as well as other challenging domains like image classification, object detection, and speech processing. In this review, we aim to present a comprehensive overview of those research methods using DL to address the AD problem. Firstly, different classifications of anomalies are introduced, and then the DL methods and architectures used for video AD are discussed and analyzed, respectively. The revised contributions have been categorized by the network type, architecture model, datasets, and performance metrics that are used to evaluate these methodologies. Moreover, several applications of video AD have been discussed. Finally, we outlined the challenges and future directions for further research in the field.

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“…Interestingly, a work efficiently discussed abnormal behavior detection [13]. In this work, the authors created LightAnomalyNet as a lightweight framework using 3D CNN of the DL algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Also, the false positive rate (FPR) is the ratio between FP and the sum of FP and TN and it is represented as Equation (13).…”

Section: Identification Approach Has Suspicious Action Does Not Have ...mentioning

confidence: 99%

Suspicious Actions Detection System Using Enhanced CNN and Surveillance Video

Selvi¹,

Adimoolam

Govindharaju

et al. 2022

Electronics

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Suspicious pre- and post-activity detection in crowded places is essential as many suspicious activities may be carried out by culprits. Usually, there will be installations of surveillance cameras. These surveillance cameras capture videos or images later investigated by authorities and post-event such suspicious activity would be detected. This leads to high human intervention to detect suspicious activity. However, there are no systems available to protect valuable things from such suspicious incidents. Nowadays machine learning (ML)- and deep learning (DL)-based pre-incident warning alarm systems could be adapted to monitor suspicious activity. Suspicious activity prediction would be based on human gestures and unusual activity detection. Even though some methods based on ML or DL have been proposed, the need for a highly accurate, highly precise, low-false-positive and low-false-negative prediction system can be enhanced by hybrid or enhanced ML- or DL-based systems. This proposed research work has introduced an enhanced convolutional neural network (ECNN)-based suspicious activity detection system. The experiment was carried out and the results were claimed. The results are analyzed with the Statistical Package for the Social Sciences (SPSS) tool. The results showed that the mean accuracy, mean precision, mean false-positive rate, and mean false-negative rate of suspicious activity detections were 97.050%, 96.743%, 2.957%, and 2.927% respectively. This result was also compared with the convolutional neural network (CNN) algorithm. This research work can be applied to enhance the pre-suspicious activity alert security system to avoid risky situations.

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LightAnomalyNet: A Lightweight Framework for Efficient Abnormal Behavior Detection

Cited by 11 publications

References 71 publications

SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions

SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions

Review on Deep Learning Approaches for Anomaly Event Detection in Video Surveillance

Suspicious Actions Detection System Using Enhanced CNN and Surveillance Video

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