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.


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Subject/Index Terms

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: USDOT 69A3551747124
  • Files: UTC, RIP
  • Created Date: Apr 1 2021 7:29PM