K. Palaniappan, and
Machine Vision and Navigation,
Springer International Publishing,
Aerial video captured from an airborne platform has an expanding range of applications including scene understanding, photogrammetry, surveying and mapping, traffic monitoring, bridge and civil infrastructure inspection, architecture and construction, delivery, disaster and emergency response, news and film, precision agriculture, and environmental monitoring and conservation. Some of the challenges in analyzing aerial video to track pedestrians, vehicles, and objects include small object size, relative motion of the object and platform, sensor jitter, and quality of imaging optics. An analytic image stabilization approach is described in this chapter where pixel information from the focal plane of the camera is stabilized and georegistered in a global reference frame. The aerial video is stabilized to maintain a fixed relative displacement between the moving platform and the scene. The proposed algorithm can be used to stabilize aerial imagery even when the available GPS and IMU measurements from the platform and sensor are inaccurate and noisy. Camera 3D poses are optimized using a homography-based robust cost function, but unlike most existing methods, the homography transformations are estimated without using any image-to-image estimation techniques. We derive a direct closed-form analytic expression from 3D camera poses that is robust even in the presence of significant scene parallax (i.e. very tall 3D buildings and man-made or natural structures). A robust non-linear least squares cost function is used to deal with outliers and speeds up computation by avoiding the use of RANdom SAmple Consensus (RANSAC). The proposed method and its efficiency is validated using several datasets and scenarios including DARPA Video and Image Retrieval and Analysis Tool (VIRAT) and high resolution wide area motion imagery (WAMI). scenarios.
author = "H. Aliakbarpour and K. Palaniappan and G. Seetharaman",
title = "Stabilization of Airborne Video Using Sensor Exterior Orientation with Analytical Homography Modeling",
year = 2020,
booktitle = "Machine Vision and Navigation",
publisher = "Springer International Publishing",
pages = "579--595",
keywords = "video stabilization, image registration, georegistration, camera parameter optimization, homography, parallax, 3d",
doi = "10.1007/978-3-030-22587-2_17",
url = "https://doi.org/10.1007/978-3-030-22587-2_17"
H. Aliakbarpour, K. Palaniappan, and G. Seetharaman. Stabilization of Airborne Video Using Sensor Exterior Orientation with Analytical Homography Modeling. Machine Vision and Navigation, Springer International Publishing, pages 579--595, 2020.