A New Context-Aware Computing Method for Urban Safety

Recently, various research efforts have been made to analyze urban environments. Particularly, predicting urban safety from by means of visual perception is very important for most people. In this paper, we propose a context-aware urban safety prediction method by measuring the contexts of urban environments through visual information. In our context-aware evaluation, we define and extract positive and negative visual associations with urban safety. Then, we add these associations to a computational model of urban safety. Our experimental results show better performance than previous approaches.