{'fraction_non_alphanumeric': 0.015050167224080268, 'fraction_numerical': 0.0, 'mean_word_length': 5.435483870967742, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Dense Optical Flow Field Estimation with Variational Refinement

Optical flow has long been a focus of research study in computer vision community. Researchers have established extensive work to solve the optical flow estimation. Among the published works, a notable work using variational energy minimization has been a baseline of optical flow estimation for a long time. Variational optical flow optimization solves an approximate global minimum in a well-defined nonlinear Markov Energy formulation. It works by first linearizing the energy model and uses a numerical method specifically successive over-relaxation (SOR) method to solve the resulting linear model. An initialization scheme is required for optical flow field in this iterative optimization method. In the original work, a zero initialization is proposed and it works well on the various environments with photometric and geometric distortion. In this work, we have experimented with different flow field initialization scheme under various environment setting. We found out that variational refinement with a good initial flow estimate using state-of-art optical flow algorithms can further improve its accuracy performance.