KEYWORDS: LIDAR, Sensors, RGB color model, 3D modeling, Data processing, Image registration, Data modeling, Associative arrays, 3D image processing, Error analysis
Three-dimensional flash LIDAR coupled with a 2D RGB camera on an aerial platform is an efficient data collection method for mapping wide-area terrain and urban sites with imagery draped over a 3D model. In order to assemble a seamless and geographically accurate mosaic product despite GPS/INS errors, frames of imagery require data-driven registration. In the approach described in this paper, all spatially overlapping frame pairs are registered, be they adjacent in time, within the same flight line, or across flight lines, and the alignment model accounts for parallax due to 3D structure. All pairwise registration constraints, along with GPS/INS measurements, are combined by least squares adjustment to estimate the pose of each frame. Registered LIDAR frames are then combined and regridded to a uniformly sampled DEM, which is then used to orthorectify and mosaic the RGB frames. Furthermore, in order to process and store hours of data efficiently, a control strategy partitions the entire terrain into moderate size tiles, within which the pairwise registration, least squares adjustment, and resampling are performed. In a flash LIDAR system designed to map 360 sq. km per hour at 1m resolution, the software will achieve near real-time throughput on a commercial PC.
High-resolution 3D imaging ladar systems can penetrate foliage and camouflage to sample fragments of concealed surfaces of interest. Samples collected while the ladar moves can be integrated into a coherent object shape, provided that sensor poses are known. We detail a system for automatic data-driven registration of ladar frames, consisting of a coarse search stage, a pairwise fine registration stage using an iterated closest points algorithm, and a multi-view registration strategy. We evaluate this approach using simulated and field-collected ladar imagery of foliage-occluded objects. Even after alignment and aggregation, it is often difficult for human observers to find, assess, and recognize objects from a point cloud display. We survey and demonstrate basic display manipulations, surface fitting techniques, and clutter suppression to enhance visual exploitation of 3D imaging ladar data.
KEYWORDS: 3D modeling, Cameras, Video, Video surveillance, 3D video streaming, 3D image processing, Imaging systems, Eye models, Sensors, Visualization
In a typical security and monitoring system a large number of networked cameras are installed at fixed positions around a site under surveillance. There is generally no global view or map that shows the guard how the views of different cameras relate to one another. Individual cameras may be equipped with pan, tilt and zoom capabilities, and the guard may be able to follow an intruder with one camera, then pick him up with another. But such tracking can be difficult, and hand off between cameras disorienting. The guard does not have the ability to continually shift his viewpoint. More over current systems do not scale up with the number of cameras. The system becomes more unwieldy as cameras are added to the system. In this paper, we will present the system and key algorithms for remote immersive monitoring of an urban site using a blanket of video cameras. The guard monitors the world using a live 3D model, which is constantly being updated from different directions using the multiple video streams. The world can be monitored remotely from any virtual viewpoint. The observer can see the entire scene from far and get a bird's eye view or can fly/zoom in and see activity of interest up close. A 3D-site model is constructed of the urban site and used as glue for combining the multiple video streams. Moreover each of the video cameras has smart image processing associated with it, which allows it to detect moving and new objects in the scene and recover their 3D geometry and pose of the camera with respect to the world model. Each video stream is overlaid on top of the video model using the recovered pose. Virtual views of the scene are generated by combining the various video streams, the background 3D model and the recovered 3D geometry of foreground objects. These moving objects are highlighted on the 3D model and used as a cue by the operator to direct his viewpoint.
We describe a technique for video compression, based on a mosaic image representation obtained from all frames in a scene sequence, giving a panoramic view of the scene. We describe two types of mosaics, static and dynamic, which are suited for storage and transmission applications, respectively. In each case, the mosaic construction process aligns the images using a global parametric motion transformation, usually canceling the effect of camera motion on the dominant portion of the scene. The residual motions that are not compensated by the parametric motion are then analyzed for their significance and coded. The mosaic representation exploits large scale spatial and temporal correlations in image sequences. In many applications where there is significant camera motion (e.g., remote surveillance), it performs substantially better than traditional interframe compression methods, and offers the potential for very low bitrate transmission. In storage applications, such as digital libraries and video editing environments, it has the additional benefit of enabling direct access and retrieval of single frames at a time.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.