| 000 | 05705nam a2200841 i 4500 | ||
|---|---|---|---|
| 001 | 6812610 | ||
| 003 | IEEE | ||
| 005 | 20200413152911.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 130917s2013 caua foab 000 0 eng d | ||
| 020 |
_a9781627051675 _qelectronic bk. |
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| 020 |
_z9781627051668 _qpbk. |
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| 024 | 7 |
_a10.2200/S00521ED1V01Y201307IVM014 _2doi |
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| 035 | _a(CaBNVSL)swl00402734 | ||
| 035 | _a(OCoLC)858583525 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aTK6680.3 _b.T453 2013 |
|
| 082 | 0 | 4 |
_a621.38928 _223 |
| 090 |
_a _bMoCl _e201307IVM014 |
||
| 100 | 1 |
_aThida, Myo., _eauthor. |
|
| 245 | 1 | 0 |
_aContextual analysis of videos / _cMyo Thida, How-lung Eng, Dorothy Monekosso, Paolo Remagnino. |
| 264 | 1 |
_aSan Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool, _c2013. |
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| 300 |
_a1 PDF (viii, 94 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 |
_aSynthesis lectures on image, video, and multimedia processing, _x1559-8144 ; _v# 14 |
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| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 500 | _aSeries from website. | ||
| 504 | _aIncludes bibliographical references (pages 77-91). | ||
| 505 | 0 | _a1. Introduction -- 1.1 Aims and objectives -- 1.2 Challenges -- 1.3 Nomenclature -- 1.4 Contributions -- 1.5 Organisation -- | |
| 505 | 8 | _a2. Literature review -- 2.1 Overview -- 2.2 Tracking multiple targets -- 2.2.1 Tracking multiple targets using particle filter -- 2.2.2 Tracking multiple targets using additional cues -- 2.2.3 Multiple-camera tracking -- 2.3 Analysis of crowd behaviour -- 2.3.1 Abnormality detection using micro-observation -- 2.3.2 Abnormality detection using macro-observation -- 2.3.3 Event detection -- 2.3.4 Graph-based and manifold learning algorithms -- 2.4 Summary -- | |
| 505 | 8 | _a3. Tracking multiple targets using particle swarm optimisation -- 3.1 Introduction -- 3.2 Literature review on particle swarm optimisation -- 3.3 Standard particle swarm optimisation -- 3.3.1 Convergence criteria -- 3.3.2 Pseudo-code -- 3.4 A modified PSO with interactive swarms -- 3.4.1 Particle and swarm diversification -- 3.4.2 Swarm optimisation -- 3.4.3 Swarm initialisation and termination -- 3.4.4 Algorithm summary -- 3.5 Experiments -- 3.5.1 Tracking fixed and known number of targets -- 3.5.2 Tracking unknown and varying number of targets -- 3.5.3 Performance evaluation -- 3.6 Summary -- | |
| 505 | 8 | _a4. Abnormality detection in crowded scenes -- 4.1 Introduction -- 4.2 Global abnormality detection -- 4.2.1 Frame-based video representation -- 4.2.2 Spatio-temporal Laplacian Eigenmaps -- 4.2.3 Analysing video manifolds in temporal domain -- 4.2.4 Experimental results -- 4.3 Local abnormality detection -- 4.3.1 Representation of local motion -- 4.3.2 Temporally constrained Laplacian Eigenmaps -- 4.3.3 Representation of regular motion pattern -- 4.3.4 Abnormality detection -- 4.3.5 Abnormality localisation -- 4.3.6 Experimental results -- 4.4 Summary -- | |
| 505 | 8 | _a5. Conclusion -- 5.1 Future directions -- Bibliography -- Authors' biographies. | |
| 506 | 1 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | |
| 510 | 0 | _aCompendex | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 520 | 3 | _aVideo context analysis is an active and vibrant research area, which provides means for extracting, analyzing and understanding behavior of a single target and multiple targets. Over the last few decades, computer vision researchers have been working to improve the accuracy and robustness of algorithms to analyze the context of a video automatically. In general, the research work in this area can be categorized into three major topics: 1) counting number of people in the scene 2) tracking individuals in a crowd and 3) understanding behavior of a single target or multiple targets in the scene. This book focuses on tracking individual targets and detecting abnormal behavior of a crowd in a complex scene | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF title page (viewed on September 17, 2013). | ||
| 650 | 0 | _aVideo surveillance. | |
| 650 | 0 |
_aAutomatic tracking _xMathematics. |
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| 650 | 0 | _aHuman activity recognition. | |
| 650 | 0 |
_aComputer vision _xMathematical models. |
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| 650 | 0 | _aImage analysis. | |
| 650 | 0 |
_aImage processing _xDigital techniques. |
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| 650 | 0 |
_aCrowds _xMathematical models. |
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| 653 | _avideo context analysis | ||
| 653 | _ainteractive swarms | ||
| 653 | _aparticle swarm optimisation | ||
| 653 | _amulti-target tracking | ||
| 653 | _asocial behavior | ||
| 653 | _acrowded scenes | ||
| 653 | _aabnormality detection | ||
| 653 | _avisual surveillance | ||
| 653 | _amanifold embedding | ||
| 653 | _acrowd analysis | ||
| 653 | _aspatio-temporal Laplacian Eigenmap | ||
| 700 | 1 |
_aEng, How-lung., _eauthor. |
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| 700 | 1 |
_aMonekosso, Dorothy., _eauthor. |
|
| 700 | 1 |
_aRemagnino, Paolo, _d1963-, _eauthor. |
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| 776 | 0 | 8 |
_iPrint version: _z9781627051668 |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on image, video, and multimedia processing ; _v# 14. _x1559-8144 |
|
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6812610 |
| 856 | 4 | 0 |
_3Abstract with links to full text _uhttp://dx.doi.org/10.2200/S00521ED1V01Y201307IVM014 |
| 999 |
_c562016 _d562016 |
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