@inbook{f92e6e64385943c999ecaf0c0c08cc91,
title = "A Methodology Review on Multi-view Pedestrian Detection",
abstract = "Although surprisingly good progress has been made in monocular pedestrian detection with emerging deep learning techniques, the existing algorithms still suffer from heavy occlusion between people. To cope with this problem, multiple cameras are often used to provide complimentary information. This chapter presents a comprehensive review on the algorithms for multi-view pedestrian detection. The existing methods are sorted into low-level fusion, intermediate-level fusion, high-level fusion and deep-learning based fusion, in terms of the degree of information fusion. The challenges in developing deep multi-view algorithms are also described in this chapter.",
keywords = "Data fusion, Multi-view, Pedestrian detection, Video surveillance",
author = "Rui Qiu and Ming Xu and Yuyao Yan and Smith, {Jeremy S.}",
note = "Funding Information: Acknowledgements This work was supported by National Natural Science Foundation of China (NSFC) under Grant 60975082 and Xi{\textquoteright}an Jiaotong-Liverpool University under Grant RDF-17-01-33. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2022",
month = may,
doi = "10.1007/978-3-030-95239-6_12",
language = "English",
series = "Studies in Big Data",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "317--339",
booktitle = "Recent Advancements in Multi-View Data Analytics",
}