Eyes on your trays 👀
Tags: IoT, Raspberry Pi, Machine Learning, TensorFlow

Problem Statement:

With the move to instill a “keep it clean” culture, NEA launched the SG Clean campaign to improve the cleanliness of public spaces and encourage good personal habits. One such habit that they are advocating is for patrons to return their trays on their own. NEA and MSE have conducted a handful of campaigns to encourage patrons to return their own trays, however, there has never been an easy way to measure the effectiveness of such programs.

The current status quo relies on having someone stationed at one section of the hawker center and manually counting the number of people who return the trays vs those who leave it. This is an extremely manual task and prone to human error, using technology allows the person to be able to do something more useful with his time. The limitation with this method also means that only a section of the hawker centre will be surveyed as well as the short time frame the person conducts the survey might lead to inaccurate data. Currently, they are only able to gather a representative sample of the population of the patron’s behaviour with this method. They are usually only able to observe 20~30 patrons within the short time they are there and the segment they are assigned to.

There are multiple stakeholders with this movement.

Cleaners — Reducing the number of tables they have to clean will help reduce the workload of the usually elderly cleaners to allow them to focus on other tasks. Patrons — The group we are trying to encourage to clean after themselves. NEA & MSE — Interested in finding out the success of their campaigns. Hawker centre managers — Interested to find out how to improve the tray return rate.

Our proposed IoT solution tries to be as non-intrusive for the patrons as possible, as well as the cleaners to allow them to continue with their current workflow. It allows NEA & MSE an efficient and more thorough way of obtaining the tray return rate data to measure the success of their campaigns. The hawker centre managers gain to gather better insights on the tray return rates to allow them to make better decisions.

IoT Solution Concept & Implementation:

Solution overview

The solution we propose is to make use of machine learning and cameras connected to Raspberry Pis. The cameras are deployed in a few key locations such as the store, tables, and tray return station. Using machine learning we are able to train a model to detect the tables, table occupancy, trays, and QR codes. The machine learning model will then send the data to the cloud which we can then use to gather insights on the patron behaviour and measure the effectiveness of the tray return campaigns.

Structure placed at hawker store to detect trays

Camera placed above the tray return station

Raspberry Pi and camera placed above the table

This allows us to gather information such as when the tray leaves the store, when it returns to the store or when it is returned to the tray return station. It allows us to measure a few key statistics such as the rate the tray is returned to a tray return station and the duration of the tray’s journey back to the store. The model also enables us to measure the occupancy of the table and how likely certain groups are to return their trays. Giving us insights on the tray return behaviour of patrons i.e which group size is most likely to return their trays.

Currently, there are not many solutions that collect data of the tray return rate but more of getting people to return their trays such as incentives and robots. None of which are able to measure the effectiveness of the tray return campaigns.

The use of other types of sensors such as RFID have been implemented previously, but to not much success. One limitation is that the sensors attached to the trays might get damaged during the intense washing process. According to the agencies, machine learning is an idea that was played around with but never tested. Setting up the cameras and a machine learning infrastructure opens up doors to collect different data points in the future to better understand the patron decisions which can help make key decisions.