This paper presents a novel framework for logo detection in low quality videos. Our method assumes the logo template is unknown in advance and exploits the property that logotype pixels appearance through several consecutive frames has a lower variance than the others. Segmentation is difficult to accomplish if logo continuity is broken. In this work we propose the use of both edge and appearance continuity to carry out the segmentation. By checking edge continuity, the video is split into sequences with stable content. Later, sequences with similar static content are merged in order to build a longer sequence. Next a Gaussian mixture is used to model the variance of the pixels values in the merged sequences. Finally, a threshold that allows identification of the logo pixels is calculated. The new method is compared with a state-of-the-art method, obtaining better results in both accuracy and false logo rejection.