TDS-Net: Transformer enhanced dual-stream network for video Anomaly Detection

Hussain, Adnan; Ullah, Waseem; Khan, Noman; Khan, Zulfiqar Ahmad; Kim, Min Je; Baik, Sung Wook

doi:10.1016/j.eswa.2024.124846

Expert Systems with Applications

2024

DOI: 10.1016/j.eswa.2024.124846

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TDS-Net: Transformer enhanced dual-stream network for video Anomaly Detection

Adnan Hussain,

Waseem Ullah,

Noman Khan

et al.

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Cited by 3 publications

References 54 publications

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STAN: Spatio-Temporal Analysis Network for efficient video action recognition

Chen,

Wang,

Sun

et al. 2024

Expert Systems with Applications

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STAN: Spatio-Temporal Analysis Network for efficient video action recognition

Chen,

Wang,

Sun

et al. 2024

Expert Systems with Applications

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Video Anomaly Detection Based on Global–Local Convolutional Autoencoder

Sun,

Zhang,

et al. 2024

Electronics

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Video anomaly detection (VAD) plays a crucial role in fields such as security, production, and transportation. To address the issue of overgeneralization in anomaly behavior prediction by deep neural networks, we propose a network called AMFCFBMem-Net (appearance and motion feature cross-fusion block memory network), which combines appearance and motion feature cross-fusion blocks. Firstly, dual encoders for appearance and motion are employed to separately extract these features, which are then integrated into the skip connection layer to mitigate the model’s tendency to predict abnormal behavior, ultimately enhancing the prediction accuracy for abnormal samples. Secondly, a motion foreground extraction module is integrated into the network to generate a foreground mask map based on speed differences, thereby widening the prediction error margin between normal and abnormal behaviors. To capture the latent features of various models for normal samples, a memory module is introduced at the bottleneck of the encoder and decoder structures. This further enhances the model’s anomaly detection capabilities and diminishes its predictive generalization towards abnormal samples. The experimental results on the UCSD Pedestrian dataset 2 (UCSD Ped2) and CUHK Avenue anomaly detection dataset (CUHK Avenue) demonstrate that, compared to current cutting-edge video anomaly detection algorithms, our proposed method achieves frame-level AUCs of 97.5% and 88.8%, respectively, effectively enhancing anomaly detection capabilities.

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AONet: Attention network with optional activation for unsupervised video anomaly detection

Rakhmonov,

Subramanian,

Amirian Varnousefaderani

et al. 2024

ETRI Journal

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Anomaly detection in video surveillance is crucial but challenging due to the rarity of irregular events and ambiguity of defining anomalies. We propose a method called AONet that utilizes a spatiotemporal module to extract spatiotemporal features efficiently, as well as a residual autoencoder equipped with an attention network for effective future frame prediction in video anomaly detection. AONet utilizes a novel activation function called OptAF that combines the strengths of the ReLU, leaky ReLU, and sigmoid functions. Furthermore, the proposed method employs a combination of robust loss functions to address various aspects of prediction errors and enhance training effectiveness. The performance of the proposed method is evaluated on three widely used benchmark datasets. The results indicate that the proposed method outperforms existing state‐of‐the‐art methods and demonstrates comparable performance, achieving area under the curve values of 97.0%, 86.9%, and 73.8% on the UCSD Ped2, CUHK Avenue, and ShanghaiTech Campus datasets, respectively. Additionally, the high speed of the proposed method enables its application to real‐time tasks.

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TDS-Net: Transformer enhanced dual-stream network for video Anomaly Detection

Cited by 3 publications

References 54 publications

STAN: Spatio-Temporal Analysis Network for efficient video action recognition

STAN: Spatio-Temporal Analysis Network for efficient video action recognition

Video Anomaly Detection Based on Global–Local Convolutional Autoencoder

AONet: Attention network with optional activation for unsupervised video anomaly detection

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