Aerial imagery of an urban environment is often characterized by significant occlusions, sharp edges, and textureless regions, leading to poor 3D reconstruction using conventional multi-view stereo methods. In this paper, we propose a novel approach to 3D reconstruction of urban areas from a set of uncalibrated aerial images. A very general structural prior is assumed that urban scenes consist mostly of planar surfaces oriented either in a horizontal or an arbitrary vertical orientation. In addition, most structural edges associated with such surfaces are also horizontal or vertical. These two assumptions provide powerful constraints on the underlying 3D geometry. The main contribution of this paper is to translate the two constraints on 3D structure into intra-image-column and inter-image-column constraints, respectively, and to formulate the dense reconstruction as a 2-pass Dynamic Programming problem, which is solved in complete parallel on a GPU. The result is an accurate cloud of 3D dense points of the underlying urban scene. Our algorithm completes the reconstruction of 1M points with 160 available discrete height levels in under a hundred seconds. Results on multiple datasets show that we are capable of preserving a high level of structural detail and visual quality.

In this paper we propose a novel approach to the detection of abandoned luggage in video surveillance. Candidates of abandoned luggage items which may pose potential security threats are first identified and localized by our proposed foreground-mask sampling technique. Our approach can deal with luggage pieces of arbitrary shape and color without the need for prior learning, and it works well under crowded and highly-cluttered situations. This localization of suspicious luggage items in the scene enables us to focus attention and subsequent processing solely on their neighborhoods. The owner of the luggage is then located and tracked to determine whether or not the luggage has been abandoned deliberately. A probability model using the MAP principle is employed to calculate a posteriori confidence score for the luggage-abandonment event, and an alarm will be automatically triggered if the certainty of luggage abandonment is higher than a pre-defined threshold. We show our results on the video datasets provided by the 2007 IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS 2007) and the 2006 IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2006).