1. A computer implemented method for measuring a performance of a surveillance system, comprising the steps of: selecting a site model, a sensor model and a traffic model respectively from a set of site models, a set of sensor models, and a set of traffic models to form a surveillance model; generating surveillance signals using the surveillance model, in which the surveillance signal includes a sequence of images; determining a quantitative performance metric for each surveillance goal in a set of qualitative surveillance goals, in which the quantitative performance metric is a number of relevant pixels in the sequence of images, and in which the relevant pixels are associated with a target object in the sequence of images, and in which the qualitative performance goals include an object detection and tracking subgoals an action recognition subgoal, and an object identification subgoal, and a likelihood function expresses a probability that the subgoal can be met for the target object at a particular instance in time as a function of the number of relevant pixels, in which the likelihood function has a form L ( n ) = P ( g | n ) = { 0 0 ≤ n ≤ n min ( n - n min ) / ( n max - n min ) n min ≤ n ≤ n max 1 n max ≤ n where n is the number of pixels, g is a subgoal, n min is a minimum number of relevant pixels, and n max is a maximum number of pixels; measuring a value for each of the quantitative performance metrics using the surveillance signals; and evaluating a performance of the surveillance system according to the values of the quantitative performance metrics measured from the surveillance signals.