TEXAS A&M

Institute of
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Applications of Computer Vision in Precision Livestock Farming

Team:

  • Nick Duffield, TAMIDS & ECE
  • Karun Kaniyamattam, Animal Science
  • Yalong Pi, TAMIDS
  • Jian Tao, TAMIDS & Performance, Visualization & Fine Arts
  • Luis Tedeschi, Department of Animal Science
  • Jack Wooley, MS Data Science
  • Siddhanth Reddy Sirgapoor, MS Data Science

Partners: Texas A&M AgriLife & Beef Center

Project: This work is pioneering the use of computer vision to transform data collection and analysis in livestock management. The project focuses on developing and refining algorithms to accurately identify and monitor animal behavior and health indicators. Using supervised, unsupervised, and statistical tools, the project has successfully demonstrated high performance in cattle behavior analysis through video monitoring, indicating a significant step forward in precision livestock farming.

Research Areas: Computer vision, Image classification, object tracking, motion detection

Data Types: Video, audio.

AI-based Vehicle and Pedestrian Traffic Measurement from Video Imaging

Team:

Former Members

Partners: Transportation Services, TTI

Project: Traffic and Pedestrian Data Analysis via Video Monitoring: Central to this project is the utilization of advanced computer vision techniques to analyze vehicle (boxes) and pedestrian (points) flow. We employ Convolutional Neural Networks (CNNs) for object/point detection and tracking algorithms for assigning detection IDs and traces. These methods were instrumental in analyzing traffic patterns around Texas A&M football games, offering insights for real-world traffic analysis to TTI and TAMIDS Transportation Service.

Research Areas: AI-based object classification; motion tracking; transportation;

Data Types: video, geospatial

Disaster Damage Assessment Using Social Media and Deep Learning

Team:

Partners: Texas A&M University at Galveston

Project: This project integrates deep learning with social media data analysis for prompt damage estimation in disaster scenarios. By employing a deep neural network (DNN) that processes OpenFEMA data and geotagged social media content, we’ve developed a model capable of performing feature extraction through natural language processing and computer vision. This model was effectively tested during Winter Storm Uri, demonstrating the potential of machine learning in enhancing disaster response and resource allocation strategies. 

Research Areas: Social media analysis, geospatial data analysis, image classification

Data Types: text, images, social media

Data & Load Modeling for Public Safety Radio Transition to LTE

Team:

  • Snehashis Chakraborty, TAMIDS & ECE
  • Nick Duffield, TAMIDS & ECE
  • Michael Fox, ITEC
  • Connor Gooch, MS in Geography
  • Walter R. Magnussen, ITEC
  • Patrick Newman, ITEC
  • Hernan D. Santos, ITEC
  • Jian Tao, TAMIDS & PVFA

Partners: Internet2 Technology Evaluation Center

Project: This project aims to gather Land Mobile Radio (LMR) data from a wide range of public safety organizations (PSOs) across the country. The primary objective is to conduct research and analysis to gain insights into the potential impacts of transitioning from LMR to LTE technology. Our project team is collaborating with ten different PSOs nationwide, acquiring not only LMR data but also relevant geographical details, including weather and event information. These data sets will serve as resources for in-depth research and analysis, allowing us to better understand LMR usage patterns and to prepare for a successful transition to LTE.

Research Areas: Mobile Radio Technology, GIS, Public Safety,

Data Types: Call records, geospatial data, infrastructure & facilities configuration

RDASH: Research Organizational Intelligence

Team:

  • Snehashis Chakraboty, TAMIDS & ECE
  • Jiaxin Du, PhD Candidate LAUP
  • Nick Duffield, TAMIDS & ECE
  • Suphanut Jamonnak, TAMIDS
  • Jian Tao, TAMIDS / VIZ / ECE / MXET
  • Xinyue Ye, LAUP, TAMIDS, & VPR

Former Members

Partners: Division of Research, University Libraries

Project: This work uses information concerning faculty publications, grant proposals, and other data to profile Texas A&M’s research in thematic research areas and to identify unexplored opportunities for future research. This project will develop and use software tools that enable exploration and characterization of current and trending research, visualized research collaborations through coauthorship networks and identify latent and emerging opportunities for collaboration through subgraph prediction.

Research Areas: graph analysis, graph clustering, link prediction, keyword analysis.

Data Types: text, bibliographic, publications, citations

Computer Vision for Detecting Clostridium difficile Spores

  • Team:
  • Nick Duffield, TAMIDS & ECE
  • Isaias Negassi, MS Data Science
  • Daniel Paredes-Sabja, Biology
  • Yalong Pi, TAMIDS
  • Jian Tao, TAMIDS & PVFA

Partners: Texas A&M Department of Biology

Project: Tackling challenges in the rapid identification of C.difficile spores, which is essential for understanding the infection process, our lab is developing a point-based deep learning computer vision system. This system is geared towards automating the 3D localization (adhered and internalized) process of the spores in confocal microscopy, especially small objects (500-1000 nm). This tool will significantly speed up the research processes of devising targeted treatments, monitoring treatment effectiveness, and preventing recurrent infections.

Research Areas: Computer vision, image segmentation, machine learning, image classification

Data Types: Imaging data

Lighthouse: A Red Flag AI Tool for Research Compliance

Team:

  • Nick Duffield, TAMIDS & ECE
  • Jian Tao, TAMIDS / VIZ / ECE / MXET
  • Tiffany Inbody, Research Compliance
  • Katherine Rojo del Busto, Research Compliance
  • Revanth Reddy Male, MS Computer Science
  • Sree Kiran Prasad Vadaga, MS Data Science

Partners:

  • TAMU Division of Research, Research Compliance

Project: Universities have responsibility to identify and regulate research in areas such as biosafety, animal subjects, human subjects. This project developed a tool to assist research compliance officers by alerting “red-flag” areas at the proposal stage. The tool used Natural Language Processing (NLP) to enhance proposal review by generating flag categories from proposal documents, and Indicating text associated with specific red-flag catrgories.

Research Areas: Natural Language Processing, Text Processing, User-Interfaces

Data Types: PDF and text files

Energy Consumption Analysis

Team:

Partners:

  • Utilities & Energy Services, Dept of Statistics

Project: In collaboration with the Texas A&M Utilities & Energy Services (UES) and the Department of Statistics, this project is to analyze the time series data from the measurement of energy consumption of a building on campus and automate the process to detect anomalies. The framework created in this project is able to help the UES detect outliers and help them with their resource allocation decisions as well as maintenance schedules. This work is based on Prophet, an open source project by Facebook to forecast time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.

Research Areas: time series analysis; time series forecasting; outlier detection 

Data Types: time series, csv files, SQL database

Bikeshare Effectiveness and Operations

Team:

  • Dan Goldberg, GeoSAT, GeoInnovation, Geography & CSE
  • Tracy Hammond, IEEI & CSE
  • Edgar Hernandez, GeoInnovation, Geography
  • Byron Prestridge, Transportation Services
  • Ron Steedly, Transportation Services
  • Shawn Turner, TTI

Partners: Transportation Services, TTI

Project: This work uses the real-time locations of TAMU rideshare bikes (starting with Ofo and now using VeoRide) and their paths to measure the effectiveness of the bikeshare program and assist with the operational rebalancing efforts to ensure that bikes are where users need them, when they need them. This project will develop software tools for data visualization and exploration, automated processes and database structures for data ingestion, storage, and retrieval, and geospatial machine learning models and approaches for predictive analytics to guide operational efforts.

Research Areas: graph analysis, big data, machine learning, visualization

Data Types: geospatial, demographic