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Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification.

H Y Swathi1, G Shivakumar2

  • 1Department of Electronics and Communication Engineering, Malnad College of Engineering, Visvesvaraya Technological University, Belagavi, India.

Mathematical Biosciences and Engineering : MBE
|July 28, 2023
PubMed
Summary

This study introduces a novel audio-visual model for crowd analysis, enhancing accuracy in challenging conditions. The hybrid approach combines visual and acoustic features for reliable crowd classification and real-time monitoring.

Keywords:
acoustic featuresaudio-visual crowd classificationdeep-spatio-temporal featuresensemble learningmulti-modal crowd analysis

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Signal Processing

Background:

  • Vision-based crowd analysis struggles with complex features and extreme conditions, leading to low accuracy.
  • Existing methods lack acoustic cues, causing ambiguity in crowd classification.
  • Integrating audio-visual data offers a path to more reliable crowd analysis.

Purpose of the Study:

  • To develop a novel audio-visual multi-modality hybrid feature learning model for crowd analysis and classification.
  • To improve the accuracy and reliability of crowd analysis, especially under challenging environmental conditions.
  • To address the limitations of vision-only approaches by incorporating acoustic information.

Main Methods:

  • Hybrid feature extraction using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet for deep spatio-temporal visual features.
  • Acoustic feature extraction including GTCC, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope, and Harmonics to Noise Ratio (HNR).
  • Fusion of audio-visual features followed by classification using a random forest ensemble classifier.

Main Results:

  • Achieved a multi-class crowd classification accuracy of 98.26%.
  • Reported high performance metrics: precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure (98.84%).
  • Demonstrated robustness and suitability for real-world crowd detection and classification tasks.

Conclusions:

  • The proposed audio-visual multi-modality model significantly enhances crowd analysis accuracy and reliability.
  • The hybrid feature learning approach effectively overcomes limitations of vision-based systems, particularly in extreme conditions.
  • The model's robustness confirms its potential for practical applications in surveillance and monitoring.