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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Fusion Object Detection and Action Recognition to Predict Violent Action.

Nelson R P Rodrigues1,2,3, Nuno M C da Costa2,3,4, César Melo2,3

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This study introduces a deep learning fusion monitoring system for shared autonomous vehicles, detecting violent actions, objects, and lost items in real-time for enhanced safety.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Automotive Safety

Background:

  • Monitoring the interior of shared autonomous vehicles (SAVs) is essential for passenger safety and security.
  • Existing systems lack comprehensive real-time monitoring capabilities for diverse in-cabin events.

Purpose of the Study:

  • To develop and evaluate a deep learning-based fusion monitoring solution for SAV interiors.
  • To integrate systems for detecting violent actions, objects, and lost items.

Main Methods:

  • Utilized public datasets (COCO, TAO) for training object detection models like YOLOv5.
  • Employed the MoLa InCar dataset for training action recognition models (I3D, R(2+1)D, SlowFast, TSN, TSM).
  • Implemented an embedded automotive solution to validate real-time performance.

Main Results:

  • Successfully trained state-of-the-art deep learning models for specific in-cabin monitoring tasks.
  • Demonstrated the feasibility of a fused system capable of real-time operation within an automotive context.

Conclusions:

  • Deep learning offers a viable approach for comprehensive in-cabin monitoring in SAVs.
  • The proposed fusion system enhances safety and security through real-time detection of critical events.