Let me in!

Inspiration

Imagine this: you're at the Ubahn station and after several minutes of waiting, finally your train arrives. However, nearly all the cars are filled with people and you just can't get in! This can/has possibly been a common occurrence for any of us during peak hours of the day.

What it does

Our solution is to give you, a passenger waiting at the station, an overview of the capacity of the cars of the incoming train. This way you see which cars are too full to enter and which ones are less filled and easier accessible. From this, you decide what part of the platform to strategically position yourself in order to access a less crowded car or to wait for the next train

How we built it

Our application was built using OpenVino, OpenCV, Python and on top of base code provided by Intel

Challenges we ran into

Inaccessibility to ready data hence we had to create these ourselves by going into the Ubahn to film videos for image detection
The fluctuation in the number of segmentation per frame
Image detection didn't not work for people obscured by a glass panel
Drawings on posters in the trains were detected as well
Installation limitations from OS

Accomplishments that we're proud of

Successfully running the image detection model ourselves
Successfully processing videos to find the free places in each car of the train.

What we learned

Thought process required to brainstorm to identify a vital problem and to come up with a viable solution
Image processing using openCv and python.
Creativity towards data collection process

What's next for Let me in!

Further develop the image detection model to exclude unwanted objects like posters and to detect people behind glass
Further development for usage in crowd control during events that attract lots of people