Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

Authors

  • Jovin Angelico Institut Teknologi Harapan Bangsa Bandung
  • Ken Ratri Retno Wardani Institut Teknologi Harapan Bangsa Bandung

DOI:

https://doi.org/10.21512/commit.v12i2.4890

Keywords:

Convolutional Neural Network, Human Detection, Tracking, RGB-D, Kalman Filter

Abstract

The computer ability to detect human being by computer vision is still being improved both in accuracy or computation time. In low-lighting condition, the detection accuracy is usually low. This research uses additional information, besides RGB channels, namely a depth map that shows objects’ distance relative to the camera. This research integrates Cascade Classifier (CC) to localize the potential object, the Convolutional Neural Network (CNN) technique to identify the human and nonhuman image, and the Kalman filter technique to track human movement. For training and testing purposes, there are two kinds of RGB-D datasets used with different points of view and lighting conditions. Both datasets have been selected to remove images which contain a lot of noises and occlusions so that during the training process it will be more directed. Using these integrated techniques, detection and tracking accuracy reach 77.7%. The impact of using Kalman filter increases computation efficiency by 41%.

Dimensions

Plum Analytics

Author Biographies

Jovin Angelico, Institut Teknologi Harapan Bangsa Bandung

Informatics Engineering

Ken Ratri Retno Wardani, Institut Teknologi Harapan Bangsa Bandung

Informatics Engineering

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Published

2018-10-31
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