A UML profile for framework modeling

INTRODUCTIONObject oriented (OO )frameworksbecamemoreandmorepopularinthesoftwareindustryduringthe1990s.Aframeworkisasetofcooper atingclassesthatmakeupareusabledesignforaspecificclassofsoftware (Deutsch ,1989;John sonandFoote,1988) .Applicationsbuiltontopofsuchaframeworkreusenotonlyitssourcecodebutalsomoreimportantitsarchitecturedesign(Pree,1994;1995;FayadandSchmidt,1997;MarkiewiczandLucena ,2 0 0 1) .Consequently,theuseofframeworksreducesthecostsandim provesthesoftwarequalitygreatly .…     

A novel method for tracking pedestrians from real-time video

INTRODUCTIONObjecttrackingisachallengingandimpor tanttaskincomputervision .Someofthemorepopularapplicationsincludevideosurveillanceinstrument,roboticcontrol,andautonomousve hicularnavigation .Trackingalgorithmsfindhowanimageregionmovesfromoneframetothenext.Thisimpliestheexistenceofanerrorfunc tiontobeminimized ,suchasthesumofsquareddifferences (SSD)betweenthetwoimagere gions.Thiserrorfunctionistheresultofmakingthe“constantbrightnessassumption” .Yet,quiteoftenweareinterestedintrackinga…     

A UML profile for framework modeling

The current standard Unified Modeling Language(UML) could not model framework flexibility and extendibility adequately due to lack of appropriate constructs to distinguish framework hot-spots from kernel elements. A new UML profile that may customize UML for framework modeling was presented using the extension mechanisms of UML, providing a group of UML extensions to meet the needs of framework modeling. In this profile, the extended class diagrams and sequence diagrams were defined to straightforwardly identify the hot-spots and describe their instantiation restrictions. A transformation model based on design patterns was also put forward, such that the profile based framework design diagrams could be automatically mapped to the corresponding implementation diagrams. It was proved that the presented profile makes framework modeling more straightforwardly and therefore easier to understand and instantiate.     

A novel method for tracking pedestrians from real-time video

This novel method of Pedestrian Tracking using Support Vector (PTSV) proposed for a video surveillance instrument combines the Support Vector Machine (SVM) classifier into an optic-flow based tracker. The traditional method using optical flow tracks objects by minimizing an intensity difference function between successive frames, while PTSV tracks objects by maximizing the SVM classification score. As the SVM classifier for object and non-object is pre-trained, there is need only to classify an image block as object or non-object without having to compare the pixel region of the tracked object in the previous frame. To account for large motions between successive frames we build pyramids from the support vectors and use a coarse-to-fine scan in the classification stage. To accelerate the training of SVM, a Sequential Minimal Optimization Method (SMO) is adopted. The results of using a kernel-PTSV for pedestrian tracking from real time video are shown at the end. Comparative experimental results showed that PTSV improves the reliability of tracking compared to that of traditional tracking method using optical flow.