C.O.M.E.T

Inspiration

Our inspiration for the project comes from the fact that many of our school mates (including myself) often have faced ludicrous amounts traffic times. Traffic is not something that only a few people feel. We aim to resolve this and improve the quality of life for everyone.

What it does

COMET is an app that tracks the number of cars (through machine learning) and returns a graph which helps to illustrate the number of vehicles/second. It helps to show areas of highly congested traffic and emission levels.

How we built it

Python for all the logic and calculations for car and time, Tkinter for GUI which helps to showcase graph). OpenCV and Haar Cascades (for algorithms and machine learning), Matplotlib (for graphs). We integrated all of it through VSC and uploaded it to git hub.

Challenges we ran into

Sometimes the GUI and the raw code wouldn't integrate properly to produce the desirable outputs. The code logic is also quite complicated so we ran into logical errors and syntax errors.

Accomplishments that we're proud of

We are proud of the GUI, the logo(handrawn btw), the concept, the time crunch, the dedication, the logic, the error fixing and the fun gartic phone sessions

What we learned

We learned teamwork, technical skills, deadline crunching and how to have fun. This was a new experience that challenged us.

What's next for C.O.M.E.T

C.O.M.E.T. hopes to join forces with the government in order to go from Bangalore to the national level for areas with high traffic. It also hopes to use the emission level knowledge in order to help the globe ecologically by offering changes by statistics.

Built With

• matplotlib
• opencv-and-haar-cascades-(for-algorithm-and-machine-learning)
• python
• tkinter-(for-gui)