The objective of this research is using video frames or highly overlapped images to obtain the camera orientation and translation parameters for trajectory estimation. One form of measurements comes from the computer vision community where successive frames from a camera approximately looking at the ground can be used to compute the translation between frames. In order to deal with the corner effect and registration problems, normalized cross correlation (NCC) is used to recognize the landmarks as the control points. The developed algorithms consist of four steps for camera trajectory estimation: (1) feature points detection and matching; (2) homography calculation; (3) control points detection and registration; (4) motion estimation. The first step is data decimation in order to reduce data amount and increase computation efficiency. Then, corner detector is employed to extract the feature points and match them between the frames using sum of absolute differences (SAD). The metric part of homography can provide the camera orientation and translation parameters according to the conjugate points between each frames. After that, this research uses RANSAC to remove the outlier of the previous step. NCC is then used to check if the camera pass thought the control points or not. This study compared the results with on-site measurement and with or without Kalman filter. Examples of applying the developed algorithm to tracking applications demonstrate the effectiveness of the methods. The example in outdoor environment indicates that the developed method for determining camera orientation and translation parameters can be used in providing initial conditions to real-time positioning and tracking in indoor or outdoor environments.