This paper focuses on camera calibration with one-dimensional (1D) objects, and novel methods are proposed in this paper. Different from the known 1D object-based camera calibration algorithms, which define the camera coordinate system as the world coordinate system, we assume that the 1D calibration object is located along the X axis of the world coordinate system. Based on this new model, a 3×2 1D homography is defined to relate the points in the 1D objects to the perspective image points thereof. Then, the basic constraint for camera calibration using 1D objects from a single image is derived. Subsequently, two existing motions, namely, rotating around a fixed point and moving on a plane, are discussed, and new algorithms are proposed. In our methods, if the number of points in the 1D objects is more than three, more compact constraints can be obtained when the 1D objects rotate around a fixed point. In the case of planar motion, the estimation of vanishing points is not needed, and the calibration accuracy is significantly improved. Finally, both computer simulations and experiments are performed to validate the effectiveness and robustness of our algorithms.