Project Grant 2415216

Award Date 9/1/24
Completion Date 8/31/27
Dollars Obligated $497K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
West Lafayette, IN, USA
Similar Awards
This Project Grant award, funded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to develop a hybrid, vision-centric framework that integrates intuitive and deliberate visual processing to create more robust visual intelligence. The $111,878 award to New York University (NYU) supports research focused on three primary directions: 1) advancing vision-centric parametric knowledge through techniques like visual...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to democratize the use of large visual learning models by reducing the computational, data, and expertise requirements needed to create and deploy such models. The $1.2M award to Georgia Tech Research Corporation will fund research on specialized learning approaches for fast model customization with limited data, efficient inference algorithms...
This Project Grant award of $399,999 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a novel multi-camera surveillance system for smart city applications. The project proposes leveraging a network of distributed smart cameras to capture and analyze streaming video data in real-time, combining edge computing and cloud resources. Key objectives include introducing innovative unsupervised learning...
This Project Grant award of $359,324, provided by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070), supports the development of advanced video anomaly anticipation technologies at Santa Clara University. The project aims to create real-time AI models capable of accurately predicting motion-based anomalies in video streams, such as those found in video surveillance systems, traffic monitoring, and industrial...
This Project Grant award of $200,000.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a novel multi-camera surveillance system for smart city applications. The project aims to create a privacy-preserving, real-time video analytics system that leverages distributed edge computing and cloud resources to address challenges in traffic mobility and public safety. Key research focus areas include...
This Project Grant award of $450,626.00 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) program supports research to develop a computer vision framework that learns and understands the physical world in a compositional manner. The key products and services to be delivered include: Establishing a unified framework for representing, parsing, and learning the compositionality of physical objects through disentangled modeling of large shape...
This Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, will support research to develop advanced profiling techniques for improving the performance of deep learning models running on Graphics Processing Units (GPUs). The $221,138 award, effective July 1, 2025 through June 30, 2030, will fund the development of three innovative analysis techniques: unified binary code analysis to identify...
This Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $125,718 to Auburn University to develop a computer vision-based algorithm for event understanding from streaming video. The key objectives are to integrate continuous deep learning representations with symbolic graph-based representations, leverage commonsense knowledge to go beyond closed-world assumptions, model event dynamics on graph...
This $150,000 federal Project Grant awarded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) program aims to develop and deploy low-cost, battery-powered smart cameras to democratize urban mobility data acquisition, especially in underserved communities. The research will focus on creating new control and perception algorithms to efficiently operate these smart cameras within battery and computational constraints. Key project elements...
This Project Grant award for $557,158.00, funded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070), supports research to devise novel mathematical operators that address the computational bottlenecks of graph-based artificial intelligence (AI) applications. The project aims to unlock sustainable and scalable performance for modern AI-based applications, such as autonomous systems, traffic forecasting, social media, drug...

This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $496,895 over a 3-year period starting September 1, 2024 to develop a unified framework for simplifying and automating the development, optimization, and adaptation of video analytics pipeline (VAP) applications.

The key products and services to be delivered under this grant include:

  1. A framework that enables domain experts to specify high-level analytics tasks and candidate physical implementations, with the framework automatically generating an optimal initial physical implementation and deploying an adaptation engine to monitor and adapt the pipeline in response to changing environmental conditions.
  2. Advancing the state-of-the-art in VAP development and deployment, significantly easing the efforts of VAP vendors and shortening time-to-deployment for new applications across sectors like transportation, healthcare, retail, and public safety.
  3. Developing general query optimization techniques that have broader applicability beyond just video analytics systems.

The project aims to have substantial societal impact by fostering wide adoption of important VAP applications and advancing the field of video analytics through innovations that will be disseminated to the broader research community and IT industry.

Generated 4/8/25, 5:41 AM