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In this paper, we present a direct application of Support Vector Machine with Augmented Features (AFSVM) for video concept detection. For each visual concept, we learn an adapted classifier by leveraging the pre-learnt SVM classifiers of other concepts. The solution of AFSVM is to re-train the SVM classifier using augmented feature, which concatenates the original feature vector with the decision value vector obtained from the pre-learnt SVM classifiers in the Reproducing Kernel Hilbert Space (RKHS). The experiments on the challenging TRECVID 2005 dataset demonstrate the effectiveness of AFSVM for video concept detection.