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Learning to Count with CNN Boosting
[chapter]
2016
Lecture Notes in Computer Science
In this paper, we address the task of object counting in images. We follow modern learning approaches in which a density map is estimated directly from the input image. We employ CNNs and incorporate two significant improvements to the state of the art methods: layered boosting and selective sampling. As a result, we manage both to increase the counting accuracy and to reduce processing time. Moreover, we show that the proposed method is effective, even in the presence of labeling errors.
doi:10.1007/978-3-319-46475-6_41
fatcat:d74atjc4yjgbphwtf42vxwj4j4