Object Tracking Methods Based On Geographic Scene And Multi-camera

With the development of society and economy, human activities become more and more frequent. This forms a more dynamic, multi-scale, uncertain and complex system. So, fast perception and monitoring of dynamic objects has become an urgent problem in regions of academia and government administration.The surveillance video is a kind of real-time and high-definition data source and contains a wealth of spatial information and attribute, which plays an important role not only in security but also in GIS. At present, the integration of surveillance videos and geospatial data are focused on static, one-way and interactive operation. It is difficult to meet the needs of the dynamic, two-way, automatic mapping between the surveillance videos and2D/3D geospatial data. And object tracking algorithms are mainly focused on the single surveillance video and it is difficult to form the continuous trajectory of the object in a large area which results in the failure of the understanding of the dynamic object’s behavior.It is necessary to develop an efficient and macroscopic analysis technology with the integration of intelligent video analysis and spatial analysis to meet the requirements for the whole view and behavior understanding of the dynamic object. Therefore, we combine videos with2D/3D geospatial data and propose some methods of object information extracting and analysis in geographical scene. The contributions are as follows:(1) The mutual mapping modal between surveillance videos and2D geospatial data based on multi-planar constrains is proposed. Current2D mutual mapping model assume the ground surface has the same elevation. When there are multiple different elevations, the existing methods need to respectively solve the matrix and the process is cumbersome. Through the comparative analysis of the differences in photogrammetry and computer vision camera model, a new mutual mapping modal is proposed and the parameters have clearly physical meaning. The modal is not only applicable to fixed cameras, but also dynamic cameras, such as PTZ cameras. Based on the modal, we analyze the characteristics of the mapping deviation under undulate ground and the resolution of surveillance videos.(2) The mutual mapping modal between surveillance videos and3D geospatial data based on depth buffer is proposed. Based on pin-hole camera modal, the3D coordinates can be converted to the corresponding image coordinates. But the transformation from the image coordinates to3D coordinates is difficult. The existing method is mainly to calculate the intersection point between the sight line and3D scene. We propose a new modal which is based on the depth buffer when3D data display in3D GIS. Through the modal, the surveillance video and the corresponding3D GIS view can achieve real-time synchronization. Based on the modal, we analyze the characteristics of the resolution of3D surveillance videos and dynamic mapping.(3) A semi-automatic mapping method between surveillance video and geospatial data is proposed. The mutual mapping uncertainty was discussed through the analysis of the homographic matrix method. In order to achieve the automatic mapping between some surveillance videos and3D GIS views, a new method is proposed, which is based on the features of vanishing points and lines.(4) A dynamic object information extracting method is proposed based on geographic scene and the corresponding data model is designed. The information contains the location and direction, geometric size, trajectories, foreground image under geospatial reference. The data modal is based on object-oriented method and is easily integrated with GIS.(5) An object trajectory estimation model is proposed. In order to get the whole trajectories in a large area where was not all covered by surveillance cameras, we combine geospatial data with the movement parameters, geometric size and color information of the object to track the object. On the one hand, through the road and the object behavior regulars, the object location can be estimated. On the other hand, the similarities including the location probability, the feature similarly of geometric size and movement parameters, images similarity can be calculated.(6) We design and develop a system for object continuous tracking. This system contains functions of the mapping between videos and geospatial data, object tracking, information extracting and trajectory estimation.