Makale detayı · 2025
Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance
- Yıl
- 2025
- ISSN
2730-7239- Tür
- article
Veri kaynağı ayrımı
- YÖKSİS YÖKSİS makale kaydı
- OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)
Özet
İngilizce (OpenAlex)
Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance.
Konular
- Anomaly Detection Techniques and Applications
- Video Surveillance and Tracking Methods
- Network Security and Intrusion Detection
Birincil konu Anomaly Detection Techniques and Applications