||Measuring displacement for large-scale structures has always been an important yet challenging task. In most applications, it is not feasible to provide a stationary platform at the location where its displacements need to be measured. And other measurement techniques like Global Positioning System (GPS) and Laser Doppler Vibrometer suffer from respective disadvantages. Recently, as the rapid development in electronics, optics and computer vision technology, image-based technique for three-dimensional (3D) displacement measurement has been developed and proven to be applicable to civil engineering structures. Most of these developments however use two or more cameras and require sophisticated calibration using a total station. The use of multiple cameras complicates the operations and synchronization becomes an important problem. Moreover, more computation effort is needed. In this study, we first present a single-camera approach that can simultaneously measure both 3D translation and rotation of a planar target attached on a structure. The intrinsic parameters of the camera are first obtained using a planar calibration board arbitrarily positioned around the target location. The obtained intrinsic parameters establish the relationship between the 3D camera coordinate and the two-dimensional image coordinate. These parameters can then be used to extract the rotation and translation of the planar target using recorded image sequence. Some derivations and discussions about the influence of camera orientation to measurement accuracy are also given. The proposed technique is illustrated using two laboratory tests and one field test. Results show that the proposed monocular videogrammetric technique is a simple and effective alternative method to measure 3D translation and rotation for civil engineering structures. In order to improve displacement measurement accuracy, a multi-rate Kalman filter is then utilized in this study to integrate the displacement measured from monocular videogrammetric technique with acceleration and a smoothing step is then followed. An outlier rejection algorithm is also introduced to prevent the filter diverging caused by some erroneous measurement from videogrammetry. Two experiments, including a shake table test are also conducted to validate the feasibility and availability of aforementioned data fusion technique. It is found that accurate displacement as well as velocity time history can hence be obtained. The de-sampling issue of videogrammetry is also discussed in the last.