| Title | Visual Assistant for Crowdsourced Anomaly Event Recognition in Smart City |
|---|---|
| ID_Doc | 40469 |
| Authors | Ho, MT; Nguyen, TT; Dao, H; Tran, MT |
| Title | Visual Assistant for Crowdsourced Anomaly Event Recognition in Smart City |
| Year | 2019 |
| Published | |
| Abstract | City anomalies such as residential fires or urban floods can cause a lot of damage each year from loss of human lives to negative effects on the productivity of the city. One possible way to deal with these anomalies in a quick and effective manner is to use the information provided by the network of citizens that is likely present at the scene of any serious anomaly. However, this source of information may be of varying quality and there may be too much data for human operators to manually inspect in a speedy manner. This motivates us to propose an architecture to effectively make use of the network of citizens to deal with city anomalies. At the center of our architecture is a neural network that automatically classify incoming image data and use this information to assist anomaly handling efforts. In order for our classifier to work well, we have also collected a dataset of city anomalies specific to Vietnam, consisting of 9963 images. We experiment with several neural network models on our collected dataset. In overall, they all have decent accuracies with the best being MobileNet with 92.3% accuracy. |
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