Picture this: Your family is in town and you’ve got the perfect evening planned out for them: first you’re gonna go to that great new restaurant downtown, take a slow stroll along the lakeside, then you’re gonna top it all off with a beautiful boat cruise at sunset. Upon reaching the restaurant, you discover that the line is out the door. Dejectedly, you and your family get in at the back of the line. You feel rushed to eat quickly, only to then discover that you missed your booking for the boat cruise.

Better yet picture this: You’re playing a friendly game of pickup basketball at your local park. The game is going well until you go out of your way to intercept the ball. Next thing you know, you’re on the ground and your arm is in absolute agony. The other players stop the game and rush you to the emergency room; only to discover that there is a massive line of people waiting to be seen. As a result, you end up having to wait for hours in agonizing pain before you finally get the treatment you so desperately need.

Now both these scenarios could have been avoided completely had they known the occupancy ahead of time so they could have planned accordingly. This is where OccuBOT comes into play. OccuBOT is web app developed to determine and compare the occupancy of multiple similar facilities. It does so by taking advantage of an open source computer vision library (OpenCV) piggybacking off of the Python coding language. The result is an easy-to-use software designed to improve the daily experiences of its users in both low and high-octane situations.


Using facial-recognition, the ML algorithm will be able to determine the number of individuals in a predefined region. This information can then be used to ascertain the occupancy of the room.

How we built it

OccuBOT is built using a python base which piggybacks off of the OpenCV library for facial recognition.

Challenges we ran into

The large majority of issues came in the form of connecting the back-end to the framework.


The applications of this web app are numerous and wide-reaching; being highly useful in both casual and emergency scenarios. OccuBOT could be used on a daily basis, with simple tasks such as determining which cafe has the shortest lines or which library is the quietest at that exact moment. This eliminates unnecessary commute time between establishments and creates a more relaxed, productive day for the user.

On the rarer but arguably more impactful side, there is the application of OccuBOT in emergency situations during there is no time to waste. If an individual is in a life-threatening state and every second is of the utmost importance, you do not want to show up to an emergency room only to discover that there is a three-hour waiting period. As such, OccuBOT could quite literally save lives by cutting precious time off of wait-times in hospitals.

OccuBOT represents an incredible avenue through which to improve users’ daily experience while also acting as an invaluable tool in time-sensitive situations, all while keeping the anonymity of the user.

Accomplishments Among the Group

Being a team composed entirely of first-time hackathon attendees, we are very happy with the end result of our 36 long hours. It has been a learning experience for all of us and we'll be leaving today with new skills and new friends.

Future Applications

In addition to OccuBOT’s aforementioned applications, there is also the option of adding further features, owing to OpenCV’s open-source recognition library. The first large feature would be that of demographic testing. By using OccuBOT, not only would the occupancy of the room be determined, but also the demographic of those within it. This would provide inexpensive information to the establishment regarding their clientele. From a commercial perspective, companies would then be able to better appeal and cater to their demographic all while seeing accurate, real-time numbers regarding who is consuming the product/service and when.

Secondly, OccuBOT presents a unique opportunity through which to improve public safety on a large scale. The facial recognition capabilities of this software, while keeping the user anonymous to other users, could be used to detect high-risk individuals and alert the appropriate authorities. This creates an non-invasive means to improve public security.


We are excited to present this project before you today and we are happy to answer any of your questions during the questioning period!

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