In this paper we address the problem of detecting reliably a subset of pedestrian targets (heads) in a high-density crowd exhibiting extreme clutter and homogeneity, with the purpose of obtaining tracking initializations. We investigate the solution provided by discriminative learning where we require that the detections in the image space be localized over most of the target area and temporally stable. The results of our tests show that discriminative learning strategies provide valuable cues about the target localization which may be combined with other complementary strategies in order to bootstrap tracking algorithms in these challenging environments.
CITATION STYLE
Aldea, E., Marastoni, D., & Kiyani, K. H. (2015). Spatio-temporal consistency for head detection in high-density scenes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9010, pp. 665–679). Springer Verlag. https://doi.org/10.1007/978-3-319-16634-6_48
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