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Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor.

Dong Seop Kim1, Muhammad Arsalan2, Kang Ryoung Park3

  • 1Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-Ro 1-Gil, Jung-Gu, Seoul 100-715, Korea. k_ds1028@naver.com.

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|March 24, 2018
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Summary
This summary is machine-generated.

This study introduces a novel convolutional neural network method for shadow detection in surveillance systems. The approach effectively overcomes limitations of visible light cameras, improving human detection accuracy in challenging outdoor environments.

Keywords:
CNNcolor featureintelligence surveillance camerashadow detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Surveillance Technology

Background:

  • Surveillance systems face challenges with human detection due to shadows in visible light images.
  • Alternative near-infrared (NIR) and thermal cameras are costly or require additional equipment.
  • Existing shadow detection methods struggle with environmental variations like illumination changes.

Purpose of the Study:

  • To develop an effective shadow detection method for visible light surveillance cameras.
  • To improve the accuracy of human detection and recognition in outdoor surveillance scenarios.
  • To overcome the limitations of current shadow detection techniques.

Main Methods:

  • Proposed a convolutional neural network (CNN)-based approach for shadow detection.
  • Utilized a custom database from diverse outdoor surveillance settings.
  • Incorporated data from the Context-Aware Vision using Image-based Active Recognition (CAVIAR) open database.

Main Results:

  • The proposed CNN-based method demonstrated superior performance compared to existing techniques.
  • Achieved enhanced accuracy in detecting and recognizing human areas despite shadow interference.
  • Validated effectiveness across various outdoor surveillance environments.

Conclusions:

  • The CNN-based shadow detection method offers a robust solution for visible light surveillance systems.
  • This approach mitigates shadow-related detection issues, enhancing overall system reliability.
  • The findings suggest a significant advancement in intelligent surveillance capabilities.