Sequential attention mechanism for weakly supervised video anomaly detection

Ullah, Waseem; Ullah, Fath U Min; Khan, Zulfiqar Ahmad; Baik, Sung Wook

doi:10.1016/j.eswa.2023.120599

Cited by 29 publications

(6 citation statements)

References 25 publications

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“…Zeng et al [16] employed a multi-instance learning framework with a spatiotemporal semantic context fusion module based on graph convolutional networks, enhancing the discrimination of video features by capturing the context of temporal and video semantics. Basak et al [17] proposed the GLFE model, which introduced a co-attention module to adaptively learn the relationships between long-term and short-term temporal features. Moreover, Liang et al [18] proposed a spatiotemporal feature fusion enhancement learning method that utilizes the temporal features between video segments.…”

Section: Weakly Supervised Video Anomaly Detectionmentioning

confidence: 99%

Normal-Abnormal Negative Impacts Suppressing Via Normal Feature Memory For Video Anomaly Detection

Chen,

Liang,

Zhan

2024

Preprint

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In the field of video anomaly detection, effective representation for the normal and abnormal video features is a key issue for improving the accuracy of detection models. The feature representation based on global spatio-temporal networks lacks to consider negative impacts for the features between normal segment and abnormal segment, resulting in incorrect detection for some segments in video. To address this issue, a new approach of normal-abnormal negative impacts suppressing via normal feature memory for video anomaly detection is proposed. In this method, feature representation with normal–abnormal negative impacts suppression via normal feature memory pool is modeled, which can be used to suppress normal-abnormal negative impacts in video feature learning. Constraint learning for normal feature memory pool is designed to promote more effective learning for the normal feature memory pool and feature of abnormal video segment. Extensive experiments demonstrate that the proposed method outperforms the relative state-of-the-art methods on the Shanghai-Tech and XD-Violence datasets and achieves competitive results on the UCF-Crime dataset. This indicates that the proposed method can effectively enhances the performance of anomaly detection, thereby validating its effectiveness in practical applications.

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Section: Weakly Supervised Video Anomaly Detectionmentioning

confidence: 99%

Normal-Abnormal Negative Impacts Suppressing Via Normal Feature Memory For Video Anomaly Detection

Chen,

Liang,

Zhan

2024

Preprint

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“…Scholars in the fields of computer vision [51,52], image recognition [53,54], and video data analysis [55][56][57][58] are presently interested in the results of new deep learning models, particularly the CNN, a branch of AI that takes its primary inspirations from the human vision system [59]. Because of sharing weights and the internal connection approach, the CNN model has demonstrated impressive results in a variety of applications, including energy forecasting, load management prediction, and numerous others.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Enhancing Short-Term Electrical Load Forecasting for Sustainable Energy Management in Low-Carbon Buildings

Alanazi,

Saeed,

Islam

et al. 2023

Sustainability

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Accurate short-term forecasting of electrical energy loads is essential for optimizing energy management in low-carbon buildings. This research presents an innovative two-stage model designed to address the unique challenges of Electricity Load Forecasting (ELF). In the first phase, robust data preprocessing techniques are employed to handle issues such as outliers, missing values, and data normalization, which are common in electricity consumption datasets in the context of low-carbon buildings. This data preprocessing enhances data quality and reliability, laying the foundation for accurate modeling. Subsequently, an advanced data-driven modeling approach is introduced. The model combines a novel residual Convolutional Neural Network (CNN) with a layered Echo State Network (ESN) to capture both spatial and temporal dependencies in the data. This innovative modeling approach improves forecasting accuracy and is tailored to the specific complexities of electrical power systems within low-carbon buildings. The model performance is rigorously evaluated using datasets from low-carbon buildings, including the Individual-Household-Electric-Power-Consumption (IHEPC) dataset from residential houses in Sceaux, Paris, and the Pennsylvania–New Jersey–Maryland (PJM) dataset. Beyond traditional benchmarks, our model undergoes comprehensive testing on data originating from ten diverse regions within the PJM dataset. The results demonstrate a significant reduction in forecasting error compared to existing state-of-the-art models. This research’s primary achievement lies in its ability to offer an efficient and adaptable solution tailored to real-world electrical power systems in low-carbon buildings, thus significantly contributing to the broader framework of modeling, simulation, and analysis within the field.

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“…Whereas Le and Kim (2023) defined deep convolutional neural network-based encoder and a multi-stage channel attention-based decoder in unsupervised learning environment. In this direction, Ullah et al (2023) developed a sequential attention mechanism for weakly supervised video anomaly detection.…”

Section: Related Workmentioning

confidence: 99%

Fight detection with spatial and channel wise attention‐based ConvLSTM model

Chaturvedi,

Dhiman,

Vishwakarma

2023

Expert Systems

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An automated detection of aggressive and violent behaviour in videos has immense potential. It enables efficient online content filtering by restricting access to extreme content and also, when integrated with security systems, helps to monitor violence in surveillance videos. In this work, a convolutional neural network is combined with the proposed Spatial and Channel wise Attention‐based ConvLSTM encoder (SCan‐ConvLSTM). The proposed architecture performs an efficient spatiotemporal fusion of the features extracted from the video sequences containing fight scenes. In order to focus selectively on regions of utmost importance, this blended attention mechanism adjusts the weights of outputs in different locations and across different channels. This recurrent attention mechanism enhances the sequential refinement of activation maps and boosts the model performance. Finally, the experimental results have been presented that show the proposed architecture achieves superior results on the benchmark datasets (RWF‐2000, Violent‐flow, Hockey‐fights, and Movies).

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Sequential attention mechanism for weakly supervised video anomaly detection

Cited by 29 publications

References 25 publications

Normal-Abnormal Negative Impacts Suppressing Via Normal Feature Memory For Video Anomaly Detection

Normal-Abnormal Negative Impacts Suppressing Via Normal Feature Memory For Video Anomaly Detection

Enhancing Short-Term Electrical Load Forecasting for Sustainable Energy Management in Low-Carbon Buildings

Fight detection with spatial and channel wise attention‐based ConvLSTM model

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