Intelligent dual stream CNN and echo state network for anomaly detection

Waseem Ullah, Tanveer Hussain, Zulfiqar Ahmad Khan, Umair Haroon, Sung Wook Baik

Research output: Contribution to journalArticle (journal)peer-review

35 Citations (Scopus)


Traditional video surveillance systems detect abnormal events via human involvement, which is exhausting and erroneous, while computer vision-based automated anomaly detection techniques replace human intervention for secure video surveillance applications. Automated anomaly detection in real-world scenarios is challenging due to diverse nature, complex, and infrequent occurrence of anomalous events. Therefore, in this paper, we propose an intelligent dual stream convolution neural network-based framework for accurate anomalous events detection in real-world surveillance scenarios. The proposed framework comprises two phases: in first phase, we develop a 2D CNN as an autoencoder, followed by a 3D visual features extraction machanism in the second phase. Autoencoder extracts spatial optimal features and forward them to echo state network to acquire a single spatial and temporal information-aware feature vector that is fused with 3D convolutional features for events patterns learning. The fused feature vector is used for anomalous events detection via a trained classifier. The proposed dual stream framework achieves significantly enhanced performance on challenging surveillance and non-surveillance anomaly and violence detection datasets.
Original languageEnglish
Article number109456
Pages (from-to)1-11
Number of pages11
JournalKnowledge-Based Systems
Early online date19 Jul 2022
Publication statusPublished - 11 Oct 2022


  • Anomaly detection
  • Echo state network
  • Weakly supervised
  • Intelligent video surveillance
  • Violence detection
  • Smart city


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