Time-lapse optical flow regularization for geophysical complex phenomena monitoring

Abstract

In this paper, we introduce a framework for the tracking of geophysical complex phenomena via time-lapse images. It includes the regularization of the derived surface motion maps time series. The proposed processing chain addresses five main challenges: undesired camera movement, missing frames, important photometric changes, weak/repetitive texture and model-free dense spatial transformations. In the proposed framework the motion maps time series are obtained via robust pre-processing steps and optical flow computing. The contribution consists of regularizing the resulting velocity and position time series to minimize a temporal closure error in a subsequent stage. This step serves to alleviate the limitations of existing methods in the context of geophysical monitoring. The temporal closure errors are formulated as linear mappings to inverse using signal priors and two formulations are defined along with illustrative cases. Related methods are discussed and extensive experimentation on simulated datasets is carried out to validate the approach and compare between the different proposed formulations and resolution schemes. Experimental results are presented on time series acquired by ground-based cameras used for the monitoring of Alpine glaciers. The algorithm is computationally efficient, even considering the quantity of processed and generated data, and is run in parallel on multiple cores for speed-up.