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Shadow detection is critical for robust and reliable vision-based systems for traffic flow analysis. In this paper we discuss various shadow detection approaches and compare two critically. The goal of these algorithms is to prevent moving shadows being misclassified as moving objects (or parts of them), thus avoiding the merging of two or more objects into one and improving the accuracy of object localization. The environment considered is an outdoor highway scene with multiple lanes observed by a single fixed camera. The important features of shadow detection algorithms and the parameter set-up are analyzed and discussed. A critical evaluation of the results both in terms of accuracy and in terms of computational complexity are outlined. Finally, possible integration of the two approaches into a robust shadow detector is presented as future direction of our research.