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An efficient framework using visual recognition for IoT based smart city surveillance.

Manish Kumar1, Kota Solomon Raju2, Dinesh Kumar1

  • 1Electronic Science Department, Kurukshetra University, Kurukshetra, Haryana India.

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|January 26, 2021
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Summary
This summary is machine-generated.

This study introduces an efficient fast subspace decomposition technique for smart city surveillance using Internet of Things (IoT) devices. The method reduces features for low-power, memory-efficient automatic face recognition, achieving minimal error rates.

Keywords:
Fast subspace decompositionFeature reductionLocal binary patternMinimum error rate

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • Smart city surveillance relies on battery-operated Internet of Things (IoT) devices.
  • Automatic face recognition in these devices demands low-power, memory-efficient visual computing systems.
  • Real-time visual recognition is crucial for smart city applications.

Purpose of the Study:

  • To propose an efficient fast subspace decomposition technique for IoT-based smart city surveillance.
  • To enhance visual recognition systems for low-power, memory-constrained devices.
  • To reduce feature redundancy for improved computational efficiency.

Main Methods:

  • Feature extraction using local binary pattern histogram.
  • Application of fast subspace decomposition over Gaussian-distributed Local Binary Pattern (LBP) features.
  • Dimension reduction for efficient visual recognition.

Main Results:

  • The proposed technique achieves higher dimension reduction, suitable for IoT surveillance.
  • Validation on well-known databases and implementation on Raspberry Pi show significant results.
  • Demonstrated least error rate with maximum feature reduction in minimum time compared to existing methods.

Conclusions:

  • The developed algorithm is effective for real-time visual recognition in smart city surveillance.
  • It offers a memory and time-efficient solution for battery-powered IoT devices.
  • The technique significantly improves the performance of automatic face recognition systems.