Comparative Study of Visual Tracking Method: A Probabilistic Approach for Pose Estimation Using Lines

Yueqiang Zhang, Xin Li, Haibo Liu, Yang Shang

Abstract: In this paper, we propose two perspective-n-line (PnL)-like methods with the presence of line detection process. Compared with the traditional methods, the proposed methods use the new error models derived from the edge points and their corresponding noisy observations rather than relying on the assumption that the noises for the two endpoints are statistically independent. Meanwhile, we improve the performance of the RAPiD-like method—another type of visual tracking approach without extracting image lines by fitting the interpolated location of the corresponding edge pixel in the local region. In addition, we compare the proposed PnL-like methods with the RAPiD-like methods and find that both the types of visual tracking methods for rigid objects are fundamentally equivalent and all of them are maximum-likelihood approaches to estimate the pose parameters, given the error model for the noisy edge points. Special consideration is put into deriving a unifying probabilistic framework to express these two types of methods. Moreover, comparisons under different performance criteria, including computational efficiency, accuracy, and robustness, are also conducted.