People detection using color and depth images

Published

Journal Article

We present a strategy that combines color and depth images to detect people in indoor environments. Similarity of image appearance and closeness in 3D position over time yield weights on the edges of a directed graph that we partition greedily into tracklets, sequences of chronologically ordered observations with high edge weights. Each tracklet is assigned the highest score that a Histograms-of-Oriented Gradients (HOG) person detector yields for observations in the tracklet. High-score tracklets are deemed to correspond to people. Our experiments show a significant improvement in both precision and recall when compared to the HOG detector alone. © 2011 Springer-Verlag Berlin Heidelberg.

Full Text

Duke Authors

Cited Authors

  • Salas, J; Tomasi, C

Published Date

  • July 14, 2011

Published In

Volume / Issue

  • 6718 LNCS /

Start / End Page

  • 127 - 135

Electronic International Standard Serial Number (EISSN)

  • 1611-3349

International Standard Serial Number (ISSN)

  • 0302-9743

Digital Object Identifier (DOI)

  • 10.1007/978-3-642-21587-2_14

Citation Source

  • Scopus