Conducting analysis on when the highest and lowest traffic occurs for retailers' stores is often difficult. We wanted to automate the process to make it easier for small/medium retailers to use this hack to better understand their target market.
What it does
This hack allows retail owners to receive a constantly updated number of customers in their store. Using motion-tracking, we can determine the point in the day when traffic reaches it peak, when traffic is low and many other analytics. With this information pushed to an accessible, beautiful IOS app, owners can effectively organize employees' work hours. Clips also double as security footage, being uploaded to Dropbox.
How I built it
Python for the back-end allows motion-tracking. Clips are uploaded to Dropbox via their API. Sketch for mockups and Swift/auto-layout for production code. Swift was also utilized for the connection with Firebase's back-end. Swift was used for the entire front end.
Challenges I ran into
Design from mock-up to X-Code was not exactly consistent due to auto-layout issues. Indico integration would have been amazing, but due to time restrictions, it was not an added feature. Due to our time constraints we weren't able to adapt the motion software to the extent we wanted which led us to eliminate certain features that would have assisted in analysis for retail owners.
Accomplishments that I'm proud of
We successfully created a back end that allowed us to detect motion and use that information to assist businesses. We're proud of our back end, the effectiveness we achieved in such a short time period, our beautiful design and our swift front end.
What I learned
Design principles, Firebase & Dropbox API as well as an increased knowledge of Swift for IOS development.
What's next for Motion Map
Facial recognition with Indico in order to track customers' moods when they enter and leave the store would be a powerful addition that could prove effective in determining the stores success.