CANavi: Synthesizing Cartoon-Like Animation for Street Navigation Based on Google Maps

Lieu Hen Chen, Hao Ming Hung, Cheng Yu Sun, Eric Hsiao Kuang Wu, Yasufumi Takama

Research output: Contribution to journalArticlepeer-review


Today, Google Maps has become one of the most important tools for exploring maps. In addition, Google Street View further provides images for improving users' cognition of new smart cities. Many route recommendation services are available online, but most provide recommended routes on a 2D planar map. This can create a gap between a user's perception and reality. It is not easy for users to understand an unfamiliar place through linked line-segments on a 2D map only. Many users find themselves lost while they attempt to virtually walkthrough an area in Google Street View via a mouse. Moreover, these street images are static and do not depict current weather conditions. Therefore, in this paper, we propose a novel approach for synthesizing cartoon-like animation for street navigation (CANavi). To achieve our goal, we utilized the abundant resources from Google Maps and Google Street View. The results show that our street-navigation animations create interesting and vivid sceneries that can successfully improve a user's cognition and experience when exploring new smart cities.

Original languageEnglish
Pages (from-to)227-238
Number of pages12
JournalIEEE Intelligent Transportation Systems Magazine
Issue number4
StatePublished - 5 Nov 2021


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