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Muon identification and high momentum measurement accuracy are crucial to fully exploit the physics potential of the ATLAS experiment at the LHC. The muon detection system of the ATLAS detector is characterized by two high precision tracking systems, namely the Inner Detector and the Muon Spectrometer, and sufficient calorimetry to ensure the safe absorption of hadrons before the spectrometer, yielding high purity muons with momenta above 3 GeV. In order to reconstruct and efficiently identify muon tracks, the MOORE and MuId Object-Oriented software packages have been developed within the ATLAS ATHENA framework. The MOORE and MuId algorithms combine to identify track segments in the Muon Spectrometer, exploit calorimeter information in order to extrapolate muons back to their production vertex, associate them with corresponding segments in the Inner Detector, and perform a combined fit to obtain optimal parameter resolution. To identify low pT muons that do not have a reconstructed Muon Spectrometer track, other strategies must be employed. We report on a method we developed to identify low pT muons and describe the offline performance studies. The simplicity of the algorithm renders it suitable for use in the ATLAS High-Level Trigger (HLT) system. We will discuss the adaptation of this algorithm for the Level Two Trigger.