In bus hijacking, the availability of instant information in the scene may help the decision-making largely. In this paper, we discussed the significant value of the information acquisition in bus hijacking emergency from a qualitative analysis and quantitative description. Furthermore, we proposed an effective emergency decision support architecture for bus hijacking based on massive information in social networks. Last but not least, as to the core part of images discrimination, we build an image anomaly detection algorithm model. In the first step of the model, we conduct a Scale Invariant Feature Transform (SIFT) detection for images, and extract local feature descriptor; In the second step, the image feature vectors of the key points are subjected to further K-means clustering, so that we get the unified K-dimensional feature vectors; In the third step, we make the image classification with Support Vector Machine (SVM) classifier. This algorithm model achieves the image discrimination for bus hijacking emergency successfully, so that the information inside the bus could be transmitted to the outside effectively, and therefore provide a significant value for emergency decision-making.
['Hua Shen', 'Xun Liang', 'Mingming Wang']
Emergency Decision Support Architectures for Bus Hijacking Based on Massive Image Anomaly Detection in Social Networks