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| Home > Publications database > A novel Voronoi-based convolutional neural network framework for pushing person detection in crowd videos |
| Journal Article | FZJ-2024-02803 |
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2024
Springer Nature
Switzerland
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Please use a persistent id in citations: doi:10.1007/s40747-024-01422-2 doi:10.34734/FZJ-2024-02803
Abstract: Analyzing the microscopic dynamics of pushing behavior within crowds can offer valuable insights into crowd patternsand interactions. By identifying instances of pushing in crowd videos, a deeper understanding of when, where, and whysuch behavior occurs can be achieved. This knowledge is crucial to creating more effective crowd management strategies,optimizing crowd flow, and enhancing overall crowd experiences. However, manually identifying pushing behavior at themicroscopic level is challenging, and the existing automatic approaches cannot detect such microscopic behavior. Thus,this article introduces a novel automatic framework for identifying pushing in videos of crowds on a microscopic level.The framework comprises two main components: (i) feature extraction and (ii) video detection. In the feature extractioncomponent, a new Voronoi-based method is developed for determining the local regions associated with each person in theinput video. Subsequently, these regions are fed into EfficientNetV1B0 Convolutional Neural Network to extract the deepfeatures of each person over time. In the second component, a combination of a fully connected layer with a Sigmoid activationfunction is employed to analyze these deep features and annotate the individuals involved in pushing within the video. Theframework is trained and evaluated on a new dataset created using six real-world experiments, including their correspondingground truths. The experimental findings demonstrate that the proposed framework outperforms state-of-the-art approaches,as well as seven baseline methods used for comparative analysis.
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