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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Related Experiment Video

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MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
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An Intelligent Cooperative Visual Sensor Network for Urban Mobility.

Giuseppe Riccardo Leone1, Davide Moroni2, Gabriele Pieri3

  • 1Institute of Information Science and Technologies, National Research Council of Italy, 56124, Pisa, Italy. g.leone@isti.cnr.it.

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|November 11, 2017
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Summary
This summary is machine-generated.

This study introduces a cooperative visual sensor network using embedded computer vision for real-time urban traffic analysis. The system enhances smart city mobility by enabling scalable data collection for traffic and parking management.

Keywords:
IoT middlewareembedded visionintelligent transportation systemsinternet of thingsreal time image processingsmart citiesvisual sensor networks

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Area of Science:

  • Computer Science
  • Urban Planning
  • Electrical Engineering

Background:

  • Smart cities require efficient traffic management solutions.
  • Intelligent video analytics on embedded sensors can provide real-time urban mobility data.
  • Existing systems often lack cooperative and adaptive analysis capabilities for comprehensive scene interpretation.

Purpose of the Study:

  • To present a cooperative visual sensor network for real-time urban traffic analysis.
  • To demonstrate a system leveraging embedded computer vision and IoT middleware for enhanced mobility insights.
  • To validate the network's effectiveness in applications like vehicular flow estimation and parking management.

Main Methods:

  • Development of a visual sensor network with embedded computer vision nodes.
  • Implementation of an Internet of Things (IoT) compliant middleware supporting in-network event composition and Machine-to-Machine (M2M) communication.
  • Cooperative and adaptive data sharing among network nodes for global scene interpretation.

Main Results:

  • The network successfully analyzes urban traffic in real time using distributed intelligence.
  • The IoT middleware facilitates seamless data sharing and cooperative analysis.
  • Field tests confirmed the system's scalability, adaptability, and extensibility for urban mobility management.

Conclusions:

  • The proposed cooperative visual sensor network offers a robust solution for smart city traffic and mobility challenges.
  • The system provides a valuable data collection layer for understanding and managing urban environments.
  • The approach is suitable for real-world deployment in smart city initiatives.