Robustnesstochangesinilluminationconditionsaswellas viewing perspectives is an important requirement formany computer vision applications. One of the key fac-ors in enhancing the robustness of dynamic scene analy-sis that of accurate and reliable means for Shadow de-ection. Shadowdetectioniscriticalforcorrectobjectde-ection in image sequences. Many algorithms have beenproposed in the literature that deal with Shadows. How-ever,acomparativeevaluationoftheexistingapproachesisstill lacking. In this paper, the full range of problems un-derlyingtheShadowdetectionareidenti?edanddiscussed.Weclassifytheproposedsolutionstothisproblemusingaaxonomyoffourmainclasses, calleddeterministicmodeland non-model based and statistical parametric and non-parametric. Novelquantitative(detectionanddiscrimina-ionaccuracy)andqualitativemetrics(sceneandobjectin-dependence,?exibilitytoShadowsituationsandrobustnesso noise) are proposed to evaluate these classes of algo-rithms on a benchmark suite of indoor and outdoor videosequences.
TinyLogin is a suite of tiny Unix utilities for handling logging into,
being authenticated by, changing one s password for, and otherwise
maintaining users and groups on an embedded system. It also provides
Shadow password support to enhance system security. TinyLogin is, as the
name implies, very small, and makes an excellent complement to BusyBox
on an embedded System. It can be used without BusyBox, of course, but I
envision them being used together most of the time.
for the TI dm6446 platformBackground subtraction moduleMaintain a background model, which can distinguish background, foreground and theft/left pixelsShadow removal moduleDetect Shadow regions in monochrome videosCamera shift detection and suppression moduleDetect of small camera shift, and suppress the false alarms due to themBlob extraction moduleExtract connected foreground and theft/left pixels as blobsTheft/left baggage distinguish moduleDistinguish theft blobs and left baggage blobs