Aiming at the problem that the existing crowd counting methods cannot achieve accurate crowd counting and map visualization in a large scene, a crowd density estimation and mapping method based on surveillance video and GIS (CDEM-M) is proposed. Firstly, a crowd semantic segmentation model (CSSM) and a crowd denoising model (CDM) suitable for high-altitude scenarios are constructed by transfer learning. Then, based on the homography matrix between the video and remote sensing image, the crowd areas in the video are projected to the map space. Finally, according to the distance from the crowd target to the camera, the camera inclination, and the area of the crowd polygon in the geographic space, a BP neural network for the crowd density estimation is constructed. The results show the following: (1) The test accuracy of the CSSM was 96.70%, and the classification accuracy of the CDM was 86.29%, which can achieve a high-precision crowd extraction in large scenes. (2) The BP neural network for the crowd density estimation was constructed, with an average error of 1.2 and a mean square error of 4.5. Compared to the density map method, the MAE and RMSE of the CDEM-M are reduced by 89.9 and 85.1, respectively, which is more suitable for a high-altitude camera. (3) The crowd polygons were filled with the corresponding number of points, and the symbol was a human icon. The crowd mapping and visual expression were realized. The CDEM-M can be used for crowd supervision in stations, shopping malls, and sports venues.

Previous VideoGIS integration methods mostly used geographic homography mapping. However, the related processing techniques were mainly for independent cameras and the software architecture was C/S, resulting in large deviations in geographic video mapping for small scenes, a lack of multi-camera video fusion, and difficulty in accessing real-time information with WebGIS. Therefore, we propose real-time web map construction based on the object height and camera posture (RTWM-HP for short). We first consider the constraint of having a similar height for each object by constructing an auxiliary plane and establishing a high-precision homography matrix (HP-HM) between the plane and the map; thus, the accuracy of geographic video mapping can be improved. Then, we map the objects in the multi-camera video with overlapping areas to geographic space and perform the object selection with the multi-camera (OS-CDD) algorithm, which includes the confidence of the object, the distance, and the angle between the objects and the center of the cameras. Further, we use the WebSocket technology to design a hybrid C/S and B/S software framework that is suitable for WebGIS integration. Experiments were carried out based on multi-camera videos and high-precision geospatial data in an office and a parking lot. The case study’s results show the following: (1) The HP-HM method can achieve the high-precision geographic mapping of objects (such as human heads and cars) with multiple cameras; (2) the OS-CDD algorithm can optimize and adjust the positions of the objects in the overlapping area and achieve a better map visualization effect; (3) RTWM-HP can publish real-time maps of objects with multiple cameras, which can be browsed in real time through point layers and hot-spot layers through WebGIS. The methods can be applied to some fields, such as person or car supervision and the flow analysis of customers or traffic passengers.

Converting tower-mounted videos from perspective to orthographic view is beneficial for their integration with maps and remote sensing images and can provide a clearer and more real-time data source for earth observation. This paper addresses the issue of low geometric accuracy in orthographic video generation by proposing a method that incorporates 3D GIS view matching. Firstly, a geometric alignment model between video frames and 3D GIS views is established through camera parameter mapping. Then, feature point detection and matching algorithms are employed to associate image coordinates with corresponding 3D spatial coordinates. Finally, an orthographic video map is generated based on the color point cloud. The results show that (1) for tower-based video, a 3D GIS constructed from publicly available DEMs and high-resolution remote sensing imagery can meet the spatialization needs of large-scale tower-mounted video data. (2) The feature point matching algorithm based on deep learning effectively achieves accurate matching between video frames and 3D GIS views. (3) Compared with the traditional method, such as the camera parameters method, the orthographic video map generated by this method has advantages in terms of geometric mapping accuracy and visualization effect. In the mountainous area, the RMSE of the control points is reduced from 137.70 m to 7.72 m. In the flat area, it is reduced from 13.52 m to 8.10 m. The proposed method can provide a near-real-time orthographic video map for smart cities, natural resource monitoring, emergency rescue, and other fields.

Aiming at the problem that it is difficult to accurately calibrate massive Pan-Tilt-Zoom Camera (PTZ) cameras on telecommunication tower and the visualization effect of orthographic geo-image is poor, this paper proposes a new method of realtime orthographic geo-image generating, which is considering Digital Elevation Model (DEM) and semantic information (ROGI-DS). First, through integrating tower cameras with 3D GIS, a camera calibration method based on view fitting (3D GIS-GeoC) is designed. Then, using the trained semantic segmentation model (TCSM), the sky area can automatically be identified and removed. Finally, based on the results of camera calibration and viewshed analysis, and the orthographic geo-image are generated. The results show that: (1) 3D GIS-GeoC method outperforms the traditional Perspective-n-Point (PnP) algorithm;(2) The tower camera semantic segmentation model (TCSM) achieves an accuracy of 96.7%; (3) ROGI-DS method improves the accuracy and visualization of orthographic geo-image under different terrain constraints, and can be used real-time monitoring of natural resources and emergency reliefs.

To address the challenges of the large-scale georeferencing of tower-based cameras and the limited capability of video-based spatial analysis, we proposed a geospatial mapping method integrating 3D GIS and gradient descent optimization. Using a Digital Elevation Model (DEM), high-resolution remote sensing imagery, and tower-based video data as the primary data sources, the proposed method first estimates the intrinsic parameters of the tower-based camera by aligning a 3D GIS virtual camera with the video imagery. Subsequently, the initial camera extrinsic parameters are estimated using the PnP algorithm based on the previously estimated intrinsic matrix $K$ and the corresponding control point pairs. Building upon these initial estimates, the camera intrinsic and extrinsic parameters are jointly optimized using a constrained L-BFGS-B framework that incorporates prior knowledge of the tower planar location, explicit box constraints, and a semi-constrained parameterization scheme with bounded parameter ranges. Furthermore, an outlier-removal and re-optimization strategy is employed to further improve the accuracy of parameter estimation. Finally, the optimized parameters are employed to transform image coordinates into three-dimensional world coordinates, and video geospatial mapping is achieved through the integration of colored point clouds with the 3D GIS scene. The results showed the following: (1) The 3D GIS scene constructed from publicly available DEM and high-resolution remote sensing imagery met the requirements for the initial estimation of intrinsic and extrinsic camera parameters. (2) Compared with PnP, RANSAC-PnP, SQPnP, and DLT, the proposed method achieves lower reprojection and 3D spatial errors. For the independent check points, the RMSE of the reprojection error is reduced by 66.4%, 73.6%, 68.0%, and 48.3%, respectively, while the RMSE of the 3D spatial error is reduced by 84.6%, 86.2%, 83.1%, and 69.4%, respectively. These results demonstrate that the proposed method provides reliable camera parameter estimates for video geospatial mapping. (3) Using the estimated camera parameters, image coordinates are transformed into 3D world coordinates to generate a georeferenced colored point cloud, which facilitates integrated analysis with existing geospatial datasets. The proposed method provides a feasible solution for tower-based camera georeferencing and three-dimensional visualization under conditions without field calibration. It offers a theoretical and technical basis for geospatial monitoring and related applications.