A Sparse Pooling Adversarial Learning Framework for Anomaly Event Detection

Detecting abnormal event in video is essential for maintaining safety in modern communities. However, due to factors of complex background, large changes in scale, and the randomness of abnormal events, causing abnormal event detection poses significant challenges. To address the issue, we propose a...

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Vydané v:	Advances in Electrical and Computer Engineering Ročník 25; číslo 2; s. 49 - 58
Hlavní autori:	ZHANG, M., HU, H., LI, Z.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Suceava Stefan cel Mare University of Suceava 01.06.2025
Predmet:	Ablation Analysis anomaly detection Artificial intelligence Automation Behavior Comparative analysis Datasets Deep learning Effectiveness encoder-decoder Frames (data processing) generative adversarial networks Image processing Learning Machine learning Methods Neural networks self-attention smart community Surveillance China
ISSN:	1582-7445, 1844-7600
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Abstract	Detecting abnormal event in video is essential for maintaining safety in modern communities. However, due to factors of complex background, large changes in scale, and the randomness of abnormal events, causing abnormal event detection poses significant challenges. To address the issue, we propose an effective sparse pooling adversarial learning framework (SPLF) for anomaly event detection, which integrates self-attention and pyramid features into a unified architecture. Specifically, the network takes video frames as input and employs an efficient U-Net to predict unknown frames. Meanwhile, self-attention mechanism and pyramid pooling features are combined to focus on salient areas and capture moving objects with varying scales. In addition, to evaluate the scores of abnormal events, a multi-scale error pyramid is introduced to improve the accuracy and robustness of the proposed SPLF. The comparison test is conducted on three publicly datasets: Ped2, Avenue, ShanghaiTech and a community scenario dataset. The frame-level AUC (area under curve) achieves 97.5%, 89.2%, 75.1% and 70.2% respectively, reaching a high level. Ablation tests further validate the effectiveness of self-attention mechanism and multi-scale pyramid pooling features. The test results demonstrate that the proposed method can effectively learn action patterns and accurately detect abnormal events in community scenarios. Index Terms--smart community, anomaly detection, encoder-decoder, generative adversarial networks, self-attention.
AbstractList	Detecting abnormal event in video is essential for maintaining safety in modern communities. However, due to factors of complex background, large changes in scale, and the randomness of abnormal events, causing abnormal event detection poses significant challenges. To address the issue, we propose an effective sparse pooling adversarial learning framework (SPLF) for anomaly event detection, which integrates self-attention and pyramid features into a unified architecture. Specifically, the network takes video frames as input and employs an efficient U-Net to predict unknown frames. Meanwhile, self-attention mechanism and pyramid pooling features are combined to focus on salient areas and capture moving objects with varying scales. In addition, to evaluate the scores of abnormal events, a multi-scale error pyramid is introduced to improve the accuracy and robustness of the proposed SPLF. The comparison test is conducted on three publicly datasets: Ped2, Avenue, ShanghaiTech and a community scenario dataset. The frame-level AUC (area under curve) achieves 97.5%, 89.2%, 75.1% and 70.2% respectively, reaching a high level. Ablation tests further validate the effectiveness of self-attention mechanism and multi-scale pyramid pooling features. The test results demonstrate that the proposed method can effectively learn action patterns and accurately detect abnormal events in community scenarios. Index Terms--smart community, anomaly detection, encoder-decoder, generative adversarial networks, self-attention. Detecting abnormal event in video is essential for maintaining safety in modern communities. However, due to factors of complex background, large changes in scale, and the randomness of abnormal events, causing abnormal event detection poses significant challenges. To address the issue, we propose an effective sparse pooling adversarial learning framework (SPLF) for anomaly event detection, which integrates self-attention and pyramid features into a unified architecture. Specifically, the network takes video frames as input and employs an efficient U-Net to predict unknown frames. Meanwhile, self-attention mechanism and pyramid pooling features are combined to focus on salient areas and capture moving objects with varying scales. In addition, to evaluate the scores of abnormal events, a multi-scale error pyramid is introduced to improve the accuracy and robustness of the proposed SPLF. The comparison test is conducted on three publicly datasets: Ped2, Avenue, ShanghaiTech and a community scenario dataset. The frame-level AUC (area under curve) achieves 97.5%, 89.2%, 75.1% and 70.2% respectively, reaching a high level. Ablation tests further validate the effectiveness of self-attention mechanism and multi-scale pyramid pooling features. The test results demonstrate that the proposed method can effectively learn action patterns and accurately detect abnormal events in community scenarios.
Audience	Academic
Author	HU, H. LI, Z. ZHANG, M.
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SubjectTerms	Ablation Analysis anomaly detection Artificial intelligence Automation Behavior Comparative analysis Datasets Deep learning Effectiveness encoder-decoder Frames (data processing) generative adversarial networks Image processing Learning Machine learning Methods Neural networks self-attention smart community Surveillance
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Title	A Sparse Pooling Adversarial Learning Framework for Anomaly Event Detection
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