Multi-sensory Features for Personnel Detection at Border Crossing

نویسندگان

  • Po-Sen Huang
  • Thyagaraju Damarla
  • Mark Hasegawa-Johnson
چکیده

Personnel detection at border crossing becomes an important issue recently. To reduce the false alarm caused by nonhuman animals or the existence of multiple objects, it is important to discriminate between humans and nonhuman animals. In this paper, based on phenomenology of the differences between humans and four-legged animals, we propose using enhanced autocorrelation pattern for feature extraction for seismic sensors, a multi-stage feature selection framework for acoustic sensors. Along with ultrasonic sensors, we use decision fusion for multi-modality fusion and use Gaussian Mixture Models for classification. From experimental results, we show that our proposed methods improve the robustness of the system.

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تاریخ انتشار 2011