People and Topics

2019 Fall Schedule

All meetings are held in EE 013.

Time

Monday

Tuesday

Wednesday

Friday

11:00-12:00

Forest

Software Engineering

Image Database

12:30-13:30

Human Behavior

13:30-14:30

Drone

Crowdsourcing

Embedded 2

14:30-15:30

Embedded 1

15:30-16:30

17:30-18:20

VIP lecture (EE 129)

Due to the large number of team mebers, it is not possible changing the regular meeting time. If your schedule does not fit into a particular team, you have to move to another team.

Topics

Forest Inventory Analysis

Use computer vision to calculate the sizes of trees (called diameter at breath height, or DBH), recognize the species of trees, and their locations. For Fall 2019, the team has two major goals: (1) handle multiple trees in a single frame and (2) handle trees in a nautral forest.

The following images show the result from a distance sensor and the tree image (before and after denoising).

forest03 forest04 forest05

The following images show how the team measures the diameter at breath height.

forest00 forest01 forest02

Readings for new members:


Analysis of Drone Video

This project creates computer vision solutions recognizing objects captured by cameras mounted on drones. In Fall 2019, the team will create a set of video clips for the following purposes:

  • Construct three-dimensional geometries of objects: The video clips will capture cardboard boxes of different sizes, together with a wide range of objects and several with known sizes.

  • Detect and track multiple moving objects: The clips include moving objects. The drone itself is also moving. The purpose is to correctly identify these objects and track their movements.

  • Segmentation: Create pixel-wise labels of different objects.

  • Re-identify people: Determine whether the same person has been before.

This project is supported by NSF CNS-1925713

Readings for new members:


Embedded Vision 1

Recent progress in computer vision has focused primarily in general-purpose object detection using datasets with many (hundreds) categories of objects (such as humans, dogs, vehicles, furniture, buildings, etc.). For many applications, however, the number of possible objects can be limited. For example, inside an airport terminal, elephants or eagles are not expected. This project will use computer graphics to synthesize images and videos of these scenarios. The synthesized data is used to train computer vision running on embedded systems (also called edge devices). Doing so can reduce network traffic and make the system more scalable. Moreover, sensitive information (such as human faces) may be detected and protected before the data leaves the cameras.

Readings for new members:

Analyze Human Behavior in Video

The purpose of this team is to use real-time video analytics to detect dangerous behavior or safety violation in workplace (such as factories), raise alerts to prevent injury, or provide post-event analysis to prevent future occurrences. In Fall 2019, the team will focus on solving these problems in an indoor environment with multiple cameras:

  • Where are the people (including re-identifying the same person in different cameras)?

  • Where does each person face?

Readings


Software Engineering for Machine Learning

This project creates a process for developing reproducible software used in machine learning. In Fall 2019, the team’s focus is to create tools that faciliate code review. The tools analyze the histories of version control repositories and automatically identify possible defects within a pull request. The tools will also collect metrics relating to the code review.

Readings


Crowdsourcing

Computer vision is still not perfect and humans outperform computers in many situations. This team builds computer tools (human interfaces) for humans to identify unexpected properties (called “bias”) in data used to train computer programs. These tools are computer games and the players (crowds) describe the characteristics in the data.

Reading for new members:

crowdsource03 crowdsource02

crowdsource05 crowdsource04


Image Database

This system integrates computer vision and database. After the objects in images are detected, the information is stored in a database so that it is searchable. The team has built a prototype of the system processing multiple video streams simultaneously. The team will focus on improving the performance (scalability) for lower latency as well as investigating new storage systems.

Reading for new members:


Embedded Vision 2

This project investigates computer vision solutions that can perform the following tasks in an embedded computer (small enough to be inside a typical camera)

  • Obtain aggregate information (such as the number of people and their genders)

  • Detect faces

  • Encrypt the faces before sending the data to storage

The sensitive data (faces) never leaves the camera. Only authorized people with the decryption key can see the faces. The concept is illustrated below.

embeddedprivacy

Readings for new members:


Faculty

https://ag.purdue.edu/ProfileImages/dbarbara.jpg

David Michael Barbarash

Landscape Architecture, Purdue

https://engineering.purdue.edu/ResourceDB/ResourceFiles/image92690

Dave Capperlleri

Mechanical Engineering, Purdue

https://shuohanchen.files.wordpress.com/2019/02/shuohan-eps-converted-to.png?w=220&h=300

Shuo-Han Chen

Institute of Information Science, Academia Sinica

https://drive.google.com/uc?id=1EqxgXBuEQNiQ5pNVvg42AfWMFKByjKh1

Yung-Hsiang Lu

Electrical and Computer Engineering, Purdue

http://www.stat.purdue.edu/images/Faculty/thumbnail/varao-t.jpg

Vinayak Rao

Statistics, Purdue

https://drive.google.com/uc?id=19_-2sKwLTcjoBvjclB8tqlIA56k5QwUq

Guofan Shao

Professor, Forestry and Natural Resources, Purdue

https://avatars1.githubusercontent.com/u/651504?s=460&v=4

George K. Thiruvathukal

Computer Science, Loyola University Chicago.

http://www.stat.purdue.edu/~mdw/images/WardMFO.jpg

Mark Daniel Ward

Statistics, Purdue

https://ag.purdue.edu/ProfileImages/woeste.jpg

Keith E. Woeste

Forestry and Natural Resources, Purdue

https://www.cs.purdue.edu/people/images/small/faculty/mingyin.jpg

Ming Yin

Computer Science, Purdue

Members

Graduate Students

https://drive.google.com/uc?id=1YunKydNN7OS_vvBubbME4UykfjNh1CgA

Abhinav Goel: Doctoral Student, Improve Neural Networks’ Energy Efficiency

https://drive.google.com/uc?id=1GzpDueX6W2e4sx0OGfKm51cru34jyEvp

Sara Aghajanzadeh: Master Student, Detect Faces and Protect Privacy

https://drive.google.com/uc?id=1kIYIrkXnICIb2odq5WWGlsdCYv4fTpVU

Ryan Dailey: Master Student, Discover Network Cameras

Undergraduate Students and 2019 Summer Teams

Image Database

https://drive.google.com/uc?id=1RWw0U_QwKhY8ZioiPdDmlN2_VEros3Zt

Shunqiao Huang

Leader

Hojoung Jang

Akshay Pawar

Aditya Chakraborty

Lucas Wiles

Dataset Distinctiveness

Identify the specific features (called distinctiveness) of different visual dataset. Use one dataset with many labels to help train machine models for another datasets with few labels.

https://drive.google.com/uc?id=1yUr73JBTlTG0LMew8pqVXA5csNggmuOX

Ashley Kim

Leader

Damini Rijhwani

https://drive.google.com/uc?id=1Qu7L33SNwQtBw8Qx-4s-Fm9oIUq9v7G-

Kirthi Shankar Sivamani

Esteban Gorostiaga

Shuhao Xing

Crowdsourcing for Data Bias

Use crowd (i.e., humans) to identify unintentional biases in visual datasets.

https://drive.google.com/uc?id=1BgdG9XYcrmdMtdSbePpp324jwdnwl_7p

Xiao Hu

Co-Leader

https://drive.google.com/uc?id=1t-krvZinKrSk1YT8MRl8R6xoPUHpF8H7

Haobo Wang

Co-Leader

https://drive.google.com/uc?id=1GSO6wVspOBuu881yg-5Bg2E5xEA1gSMJ

Kaiwen Yu

https://drive.google.com/uc?id=1u5dbejyw-62y5x6UPKEtPo3DFd4AtYCc

Anirudh Vegesana

Somesh Dube

Forest Inventory

Use computer vision to calculate the sizes of trees (called diameter at breath height, or DBH).

https://drive.google.com/uc?id=1GeeVgSnl4Fwf-rlIFlG5LuSohcMMIpTi

Nick Eliopoulos

https://drive.google.com/uc?id=1WrLZtXkzgHDQbCC0XLX92C8a8rgS6yMd

Yezhi Shen

Yuxin Zhang

Vaastav Arora

Minh Nguyen

Human Behavior

Track human activities and understand how they use designed space.

https://drive.google.com/uc?id=14FxQ_dr9836vXFBx1YknDQ0rn-QVZHWy

Mohamad Alani

https://drive.google.com/uc?id=1bZxvHiZ-H7ACq55FpJQqbJgj8NZjZlcb

Peter Huang

Dhruv Swarup

Chau Minh Nguyen

Alumni

Video by Current and Former Members