Matches in SemOpenAlex for { <https://semopenalex.org/work/W2343747075> ?p ?o ?g. }
- W2343747075 endingPage "4158" @default.
- W2343747075 startingPage "4144" @default.
- W2343747075 abstract "This paper proposes an effective multi-view learning approach to foreground detection for traffic surveillance applications. This approach involves three main steps. First, a reference background image is generated via temporal median filtering, and multiple heterogeneous features (including brightness variation, chromaticity variation, and texture variation, each of which represents a unique view) are extracted from the video sequence. Then, a multi-view learning strategy is devised to online estimate the conditional probability densities for both the foreground and the background. The probability densities of three features are approximately conditionally independent and are estimated with kernel density estimation. Pixel soft labeling is conducted by using Bayes rule, and the pixelwise foreground posteriors are computed. Finally, a Markov random field is constructed to incorporate the spatiotemporal context into the foreground/background decision model. The belief propagation algorithm is used to label each pixel of the current frame. Experimental results verify that the proposed approach is effective to detect foreground objects from challenging traffic environments and outperforms some state-of-the-art methods." @default.
- W2343747075 created "2016-06-24" @default.
- W2343747075 creator A5007275423 @default.
- W2343747075 creator A5029944675 @default.
- W2343747075 creator A5042895349 @default.
- W2343747075 creator A5083438052 @default.
- W2343747075 date "2016-06-01" @default.
- W2343747075 modified "2023-09-24" @default.
- W2343747075 title "A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications" @default.
- W2343747075 cites W1988061476 @default.
- W2343747075 cites W1991127174 @default.
- W2343747075 cites W1991314183 @default.
- W2343747075 cites W1995903777 @default.
- W2343747075 cites W1997932664 @default.
- W2343747075 cites W2014324073 @default.
- W2343747075 cites W2026938603 @default.
- W2343747075 cites W2031708389 @default.
- W2343747075 cites W2033453961 @default.
- W2343747075 cites W2051825853 @default.
- W2343747075 cites W2054883883 @default.
- W2343747075 cites W2059639989 @default.
- W2343747075 cites W2065870102 @default.
- W2343747075 cites W2066021222 @default.
- W2343747075 cites W2069057506 @default.
- W2343747075 cites W2071860582 @default.
- W2343747075 cites W2082834618 @default.
- W2343747075 cites W2095581126 @default.
- W2343747075 cites W2098305432 @default.
- W2343747075 cites W2098762572 @default.
- W2343747075 cites W2102625004 @default.
- W2343747075 cites W2108196201 @default.
- W2343747075 cites W2111918405 @default.
- W2343747075 cites W2112157293 @default.
- W2343747075 cites W2115213191 @default.
- W2343747075 cites W2115415549 @default.
- W2343747075 cites W2116076678 @default.
- W2343747075 cites W2118143383 @default.
- W2343747075 cites W2124082557 @default.
- W2343747075 cites W2127070222 @default.
- W2343747075 cites W2130260536 @default.
- W2343747075 cites W2130411857 @default.
- W2343747075 cites W2134576786 @default.
- W2343747075 cites W2139232681 @default.
- W2343747075 cites W2140235142 @default.
- W2343747075 cites W2141572457 @default.
- W2343747075 cites W2148290050 @default.
- W2343747075 cites W2150489380 @default.
- W2343747075 cites W2154779066 @default.
- W2343747075 cites W2164734564 @default.
- W2343747075 cites W2165927991 @default.
- W2343747075 cites W2166978545 @default.
- W2343747075 cites W4231795354 @default.
- W2343747075 doi "https://doi.org/10.1109/tvt.2015.2509465" @default.
- W2343747075 hasPublicationYear "2016" @default.
- W2343747075 type Work @default.
- W2343747075 sameAs 2343747075 @default.
- W2343747075 citedByCount "38" @default.
- W2343747075 countsByYear W23437470752017 @default.
- W2343747075 countsByYear W23437470752018 @default.
- W2343747075 countsByYear W23437470752019 @default.
- W2343747075 countsByYear W23437470752020 @default.
- W2343747075 countsByYear W23437470752021 @default.
- W2343747075 countsByYear W23437470752022 @default.
- W2343747075 countsByYear W23437470752023 @default.
- W2343747075 crossrefType "journal-article" @default.
- W2343747075 hasAuthorship W2343747075A5007275423 @default.
- W2343747075 hasAuthorship W2343747075A5029944675 @default.
- W2343747075 hasAuthorship W2343747075A5042895349 @default.
- W2343747075 hasAuthorship W2343747075A5083438052 @default.
- W2343747075 hasConcept C105795698 @default.
- W2343747075 hasConcept C114614502 @default.
- W2343747075 hasConcept C115961682 @default.
- W2343747075 hasConcept C121332964 @default.
- W2343747075 hasConcept C124504099 @default.
- W2343747075 hasConcept C126042441 @default.
- W2343747075 hasConcept C151730666 @default.
- W2343747075 hasConcept C152565575 @default.
- W2343747075 hasConcept C153180895 @default.
- W2343747075 hasConcept C154945302 @default.
- W2343747075 hasConcept C160633673 @default.
- W2343747075 hasConcept C185429906 @default.
- W2343747075 hasConcept C2778045648 @default.
- W2343747075 hasConcept C2778334786 @default.
- W2343747075 hasConcept C2779343474 @default.
- W2343747075 hasConcept C2779769447 @default.
- W2343747075 hasConcept C31972630 @default.
- W2343747075 hasConcept C32653426 @default.
- W2343747075 hasConcept C33923547 @default.
- W2343747075 hasConcept C41008148 @default.
- W2343747075 hasConcept C44870925 @default.
- W2343747075 hasConcept C71134354 @default.
- W2343747075 hasConcept C74193536 @default.
- W2343747075 hasConcept C76155785 @default.
- W2343747075 hasConcept C86803240 @default.
- W2343747075 hasConceptScore W2343747075C105795698 @default.
- W2343747075 hasConceptScore W2343747075C114614502 @default.
- W2343747075 hasConceptScore W2343747075C115961682 @default.
- W2343747075 hasConceptScore W2343747075C121332964 @default.