000 02366nam a22004575i 4500
001 978-1-4471-4730-5
003 DE-He213
005 20200421111842.0
007 cr nn 008mamaa
008 121227s2013 xxk| s |||| 0|eng d
020 _a9781447147305
_9978-1-4471-4730-5
024 7 _a10.1007/978-1-4471-4730-5
_2doi
050 4 _aQ337.5
050 4 _aTK7882.P3
072 7 _aUYQP
_2bicssc
072 7 _aCOM016000
_2bisacsh
082 0 4 _a006.4
_223
100 1 _aAhad, Md. Atiqur Rahman.
_eauthor.
245 1 0 _aMotion History Images for Action Recognition and Understanding
_h[electronic resource] /
_cby Md. Atiqur Rahman Ahad.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2013.
300 _aXVI, 116 p. 34 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 _aIntroduction -- Action Representation -- Motion History Image -- Action Datasets and MHI.
520 _aHuman action analysis and recognition is a relatively mature field, yet one which is often not well understood by students and researchers.  The large number of possible variations in human motion and appearance, camera viewpoint, and environment, present considerable challenges.  Some important and common problems remain unsolved by the computer vision community. However, many valuable approaches have been proposed over the past decade, including the motion history image (MHI) method. This method has received significant attention, as it offers greater robustness and performance than other techniques. This work presents a comprehensive review of these state-of-the-art approaches and their applications, with a particular focus on the MHI method and its variants.
650 0 _aComputer science.
650 0 _aPattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aPattern Recognition.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447147299
830 0 _aSpringerBriefs in Computer Science,
_x2191-5768
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-4730-5
912 _aZDB-2-SCS
942 _cEBK
999 _c55646
_d55646