The GUI app detects obstacles to reduce uncertainty in people’s commute. We used computer vision and other APIs to notify users at the right time. For example, when there are too many people at the subway station and trains are too crowded, we may alert a user who might be thinking about taking the subway. This way users can think about alternative ways like taking an Uber or bike. The project may have a strong use case for mobility impaired individuals for whom even minor incidents can have a significant impact on their commute [1,2].
Process
Our design process started with understanding the literature on Smart Cities. We then talked to experts to learn more about the shortcomings and advantages of using technologies in this space. While narrowing down the scope, we developed multiple prototypes and simultaneously conducted user research to build empathy with our end users (commuters). Finally, I developed a working prototype of our ideas that we presented to our stakeholders.
System Design
Computer Vision to detect anomalies
Team
Code
The code can be found here.