000			03343nam a22004935i 4500
001			978-1-4614-7987-1
003			DE-He213
005			20200421112226.0
007			cr nn 008mamaa
008			130831s2014 xxu| s |||| 0|eng d
020			_a9781461479871 _9978-1-4614-7987-1
024	7		_a10.1007/978-1-4614-7987-1 _2doi
050		4	_aT385
050		4	_aTA1637-1638
050		4	_aTK7882.P3
072		7	_aUYQV _2bicssc
072		7	_aCOM016000 _2bisacsh
082	0	4	_a006.6 _223
100	1		_aGer�onimo, David. _eauthor.
245	1	0	_aVision-based Pedestrian Protection Systems for Intelligent Vehicles _h[electronic resource] / _cby David Ger�onimo, Antonio M. L�opez.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2014.
300			_aX, 114 p. 42 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Computer Science, _x2191-5768
505	0		_a1. Introduction -- 2. Candidates Generation -- 3. Classification -- 4. Completing the System -- 5. Datasets and Benchmarking -- 6. Conclusions.
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.
650		0	_aComputer science.
650		0	_aComputer graphics.
650	1	4	_aComputer Science.
650	2	4	_aComputer Imaging, Vision, Pattern Recognition and Graphics.
650	2	4	_aSignal, Image and Speech Processing.
700	1		_aL�opez, Antonio M. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461479864
830		0	_aSpringerBriefs in Computer Science, _x2191-5768
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4614-7987-1
912			_aZDB-2-SCS
942			_cEBK
999			_c57665 _d57665