Semi-Supervised Crowd Counting from Unlabeled Data

arXiv:2108.13969v3 Announce Type: replace-cross
Abstract: Automatic Crowd behavior analysis can be applied to effectively help the daily transportation statistics and planning, which helps the smart city construction. As one of the most important keys, crowd counting has drawn increasing attention. Recent works achieved promising performance but relied on the supervised paradigm with expensive crowd annotations. To alleviate the annotation cost in real-world transportation scenarios, in this work we proposed a semi-supervised learning framework $S^{4}\textit{Crowd}$, which can leverage both unlabeled/labeled data …

abstract analysis arxiv attention behavior behavior analysis city construction cs.cv cs.lg daily data keys paradigm performance planning semi-supervised smart smart city statistics transportation type