Exploring AI-based Video Segmentation and Saliency Computation to Optimize Imagery-acquisition from Moving Vehicles

In this project, the research team proposes to employ machine learning (ML) techniques for creating adaptive sampling profiles and a data-driven, opportunistic approach to data acquisition from moving sensors. The immediate goal is to drastically cut down the cost of deploying video and image sensors, making them more practical. To this end, the team plans to explore a novel research direction: detecting the salient frames in video data captured by sensors using computer vision, video segmentation algorithms. Then, a data-driven approach using ML will be employed to find the control features that enhance sensor data acquisition and prevent huge waste to the memory and storage resources. The team plans to evaluate their proposed methods by demonstrating their effectiveness in a pedestrian mobility analysis. The team provides a method to count pedestrians from a moving car instead of relying on the conventional methods of using fixed sensors or human counters, which due to their high cost, suffer from very limited spatial coverage.

• Record URL:
  • https://c2smart.engineering.nyu.edu/exploring-ai-based-video-segmentation-and-saliency-computation-to-optimize-imagery-acquisition-from-moving-vehicles/

Language

• English

Project

• Status: Completed
• Funding: $133,286
• Contract Numbers:

  69A3551747124

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Connected Cities for Smart Mobility towards Accessible and Resilient Transportation Center (C2SMART)

  New York University
  Tandon School of Engineering
  Brooklyn, NY  United States 
• Performing Organizations:

  Connected Cities for Smart Mobility towards Accessible and Resilient Transportation Center (C2SMART)

  New York University
  Tandon School of Engineering
  Brooklyn, NY  United States 
• Principal Investigators:

  Silva, Claudio

• Start Date: 20210301
• Expected Completion Date: 20231031
• Actual Completion Date: 20231031
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Computer vision; Data collection; Image processing; Machine learning; Pedestrian counts; Sensors; Video
• Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists;

Filing Info

• Accession Number: 01768982
• Record Type: Research project
• Source Agency: Connected Cities for Smart Mobility towards Accessible and Resilient Transportation Center (C2SMART)
• Contract Numbers: 69A3551747124
• Files: UTC, RIP
• Created Date: Apr 1 2021 7:29PM