MPS: Multi-Person Shopping for Cashier-Less Store

This project shares our solution for AiFi's CPS-IoT Autocheckout Competition. We're the winner: Team 3!

• Authors: Yixin Bao, Xinyue Cao, Chenghui Li, Mengmeng Zhang
• Affiliation: Carnegie Mellon University, U.S.

Demo

============= Our Predicted Receipt  =============
Customer ID: 14322669897997084492

Purchase List: 
9 x Boomchickapop Sweet & Salty Kettle Corn
6 x Boomchickapop Sea Salt Popcorn
6 x Skinnypop Popcorn

F1-score: 97.6%

Installation

• Install and start mongodb in order to store the test case data

sudo systemctl start mongod

• Install dependencies

pip3 install -r requirements.txt

Sample Data

• Download Videos Here (17.1MB)

• Download Data without Depth Images Here (239MB)

• Download Data with Depth Images Here (2.0GB)

• The complete public datasets available at http://aifi.io/research under Sample Data.

• To import the data into mongodb:

mongorestore --archive="cps-test-01-nodepth.archive"

Get started

To test one single testcase:

python3 test.py

To get more detaild log, change in config.py:

VERBOSE = 1

To test it against the competition API:

python3 submit.py

Ground truth

For most of testcases in public dataset and the competition datast, we have manually labeled the ground truth.

To get a F1-score out of the ground truth, modify the main function in evaluation.py to include your target database, then:

python3 evaluation.py

Documentation & Benchmark Results

If you're interested in our methodologies and benchmark results, please refer to our Report.

Our team

Citing MPS

If you use MPS in your research or wish to refer to the baseline results published in Report, please use the following BibTeX entry.

@unpublished{MPS2020,
  author = {Yixin Bao, Xinyue Cao, Chenghui Li, Mengmeng Zhang},
  title = {Multi-Person Shopping (MPS) for Cashier-Less Store},
  school = {Carnegie Mellon University},
  year = {2020},
  note = {Unpublished: https://github.com/AutoCheckout-CMU/AutoCheckout}
}