In video surveillance, the most important objects are people and vehicles. This workshop will focus on vehicles. Specifically, we will explore solutions to the practical problem of finding a given vehicle from an array of surveillance cameras using criteria such as the size, model, or color. Other potentially differentiating characteristics include whether a vehicle is a taxi, a delivery truck or a police car. Furthermore, a search might be based on the location of the vehicle (i.e. north side of the highway, in the parking area behind the building) or the behavior of the vehicle (i.e. speed, direction of travel, whether the vehicle is parked illegally, or involvement in an accident or drive-by shooting.) In the last few years, a number of studies have been undertaken to classify vehicles according to their type, make, model or color. However, the evaluation of each of these classification methods has been performed on in-house datasets. Since each dataset involves its own camera viewpoints, lighting conditions and resolutions, there has been no way to compare the relative merits of these methods.