Crowd Anomaly Detection Based on Optical Flow, Artificial Bacteria Colony and Kohonen’s Neural Network

  • Joelmir RamosEmail author
  • Nadia Nedjah
  • Luiza M. Mourelle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10405)


This paper presents a novel method for global anomaly detection in crowded scenes. The optical flow of frames is used to extract the foreground of areas with people motions in crowd. The optical flow between two frames generates one layer. The proposed method applies the metaheuristic of artificial bacteria colony as a robust algorithm to optimize the layers from optical flow. The artificial bacteria colony has the ability to adapt quickly to the most varied scenarios, extracting just relevant information from regions of interest. Moreover, the algorithm has low sensibility to noise and to sudden changes in video lighting as captured by optical flow. The bacteria population of colonies, its food storage and the colony’s centroid position regarding each optical flow layer, are used as input to train a Kohonen’s neural network. Once trained the network is able to detect specific events based on behavior patterns similarity, as produced by the bacteria colony during such events. Experiments are conducted on publicly available dataset. The achieved results show that the proposed method captures the dynamics of the crowd behavior successfully, revealing that the proposed scheme outperforms the available state-of-the-art algorithms for global anomaly detection.


Crowd anomaly detection Optical flow Artificial bacteria colony Kohonen’s neural network 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Joelmir Ramos
    • 1
    Email author
  • Nadia Nedjah
    • 2
  • Luiza M. Mourelle
    • 3
  1. 1.Post-Graduation Program in Electronics EngineeringState University of Rio de JaneiroRio de JaneiroBrazil
  2. 2.Department of Electronics Engineering and TelecommunicationsState University of Rio de JaneiroRio de JaneiroBrazil
  3. 3.Department of Systems Engineering and ComputationState University of Rio de JaneiroRio de JaneiroBrazil

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