The amount of multimedia in existence has become so extensive that the organization of this data cannot be performed manually. Systems designed to maintain such quantity need superior methods of understanding the information contained in the data. Aspects of Computer Vision deal with such problems for the understanding of image and video content. Additionally large ontologies such as LSCOM are collections of feasible high-level concepts that are of interest to identify within multimedia content. While ontologies often include the activity of dance it has had virtually no coverage in Computer Vision literature in terms of actual detection. We will demonstrate the fact that training based approaches are challenged by dance because the activity is defined by an unlimited set of movements and therefore unreasonable amounts of training data would be required to recognize even a small portion of the immense possibilities for dance. In this thesis we present a non-training, tempo based approach to dance detection which yields very good results when compared to another method with state-of-the-art performance for other common activities; the testing dataset contains videos acquired mostly through YouTube. The algorithm is based on one dimensional analysis in which we perform visual beat detection through the computation of optical flow. Next we obtain a set of tempo hypotheses and the final stage of our method tracks visual beats through a video sequence in order to determine the most likely tempo for the object motion. In this thesis we will not only demonstrate the utility for visual beats in visual tempo detection but we will demonstrate their existence in most of the common activities considered by state-of-the-art methods.