Appropriate segmentation of video is a key step for applications such as video surveillance, video composing, video compression, storage and retrieval, and automated target recognition. Video segmentation algorithms involve dissecting the video into scenes based on shot boundaries as well as local objects and events based on spatial shape and regional motions. Many algorithmic approaches to video segmentation have been recently reported, but many lack measures to quantify the success of the segmentation especially in comparison to other algorithms. This paper suggests multiple bench-top measures for evaluating video segmentation. The paper suggests that the measures are most useful when 'truth' data about the video is available such as precise frame-by- frame object shape. When precise 'truth' data is unavailable, this paper suggests using hand-segmented 'truth' data to measure the success of the video segmentation. Thereby, the ability of the video segmentation algorithm to achieve the same quality of segmentation as the human is obtained in the form of a variance in multiple measures. The paper introduces a suite of measures, each scaled from zero to one. A score of one on a particular measure is a perfect score for a singular segmentation measure. Measures are introduced to evaluate the ability of a segmentation algorithm to correctly detect shot boundaries, to correctly determine spatial shape and to correctly determine temporal shape. The usefulness of the measures are demonstrated on a simple segmenter designed to detect and segment a ping pong ball from a table tennis image sequence.