Accurate Efficient Mosaicking for Wide Area Aerial Surveillance
Wide area aerial surveillance datasets are often captured by an array of cameras. Mosaicking (or stitching) the array in every frame of the video is the first step in video content analysis. Our idea based on a piecewise affine image deformation model is able to produce high quality mosaics while still remaining computationally efficient.
In this work we consider the problem of tracking objects from a moving airborne platform through long occlusions and/or when their motion is unpredictable. The main idea is to take advantage of the known 3D scene structure to estimate a dynamic occlusion map and link fragmented tracks by solving a sequence alignment problem.
Here we developed an algorithm for tracking of vehicles in aerial surveillance imagery. The main idea is to formulate the data association problem as inference in a set of Bayesian networks, which offers a number of advantages. Results show low track fragmentation and high computational efficiency.
To classify vehicles from arbitrary viewpoint in video, we propose to estimate the moving vehicle pose using structure from motion and match a database of 3D models projected to the same pose.