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.
CITATION STYLE
Salas, J., & Tomasi, C. (2011). People detection using color and depth images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6718 LNCS, pp. 127–135). https://doi.org/10.1007/978-3-642-21587-2_14
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