People Detection Using Color and Depth Images

9 years 12 days ago
People Detection Using Color and Depth Images
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.
Joaquín Salas, Carlo Tomasi
Added 16 Sep 2011
Updated 16 Sep 2011
Type Journal
Year 2011
Where MCPR2
Authors Joaquín Salas, Carlo Tomasi
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