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000695355 020__ $$a9781461479871 $$qelectronic book
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000695355 0247_ $$a10.1007/978-1-4614-7987-1$$2doi
000695355 035__ $$aSP(OCoLC)ocn857905225
000695355 035__ $$aSP(OCoLC)857905225
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000695355 08204 $$a006.3/7$$223
000695355 1001_ $$aGerónimo, David,$$eauthor.
000695355 24510 $$aVision-based pedestrian protection systems for intelligent vehicles$$h[electronic resource]  /$$cDavid Gerónimo, Antonio M. López.
000695355 264_1 $$aNew York, New York :$$bSpringer,$$c[2013?]
000695355 264_4 $$c©2014
000695355 300__ $$a1 online resource (x, 114 pages) :$$billustrations.
000695355 336__ $$atext$$btxt$$2rdacontent
000695355 337__ $$acomputer$$bc$$2rdamedia
000695355 338__ $$aonline resource$$bcr$$2rdacarrier
000695355 4901_ $$aSpringerBriefs in computer science,$$x2191-5768
000695355 504__ $$aIncludes bibliographical references.
000695355 5050_ $$a1. Introduction -- 2. Candidates Generation -- 3. Classification -- 4. Completing the System -- 5. Datasets and Benchmarking -- 6. Conclusions.
000695355 506__ $$aAccess limited to authorized users.
000695355 520__ $$aPedestrian Protection Systems (PPSs) are on-board systems aimed at detecting and tracking people in the surroundings of a vehicle in order to avoid potentially dangerous situations. These systems, together with other Advanced Driver Assistance Systems (ADAS) such as lane departure warning or adaptive cruise control, are one of the most promising ways to improve traffic safety. By the use of computer vision, cameras working either in the visible or infra-red spectra have been demonstrated as a reliable sensor to perform this task. Nevertheless, the variability of human's appearance, not only in terms of clothing and sizes but also as a result of their dynamic shape, makes pedestrians one of the most complex classes even for computer vision. Moreover, the unstructured changing and unpredictable environment in which such on-board systems must work makes detection a difficult task to be carried out with the demanded robustness. In this brief, the state of the art in PPSs is introduced through the review of the most relevant papers of the last decade. A common computational architecture is presented as a framework to organize each method according to its main contribution. More than 300 papers are referenced, most of them addressing pedestrian detection and others corresponding to the descriptors (features), pedestrian models, and learning machines used. In addition, an overview of topics such as real-time aspects, systems benchmarking and future challenges of this research area are presented.
000695355 588__ $$aDescription based on online resource; title from PDF title page (SpringerLink, viewed September 3, 2013).
000695355 650_0 $$aComputer vision.
000695355 650_0 $$aDriver assistance systems.
000695355 650_0 $$aAutomotive sensors.
000695355 7001_ $$aLópez, Antonio M.,$$eauthor.
000695355 830_0 $$aSpringerBriefs in computer science,$$x2191-5768
000695355 85280 $$bebk$$hSpringerLink
000695355 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-1-4614-7987-1$$zOnline Access
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