In this work, we investigate a method for detection of nudity in videos based on a bag-of-visual-feature representation for frames and an associated voting scheme. Our results showed that our approach is indeed able to provide good recognition rates for nudity even at the frame level and with a relatively low sampling ratio. Also, the proposed voting scheme significantly enhances the recognition rates for video segments a value of 93.2% of correct classification. Also, to support this task, we developed our own database of nude and non-nude videos.