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When ever an object moves, it successively covers and uncovers surfaces that are farther away. This occlusion and dis-occlusion always occurs precisely at the boundaries of the moving object and as such provide information not only about the shape of the object but also about its velocity, transparency, and relative depth. Humans can and do use this information, and the process has come to be called Spatiotemporal Boundary Formation (SBF). Previous authors have used the wealth of experimental investigations into SBF to create a mathematical model of the process. In this article we proposed a novel method to recover the orientation and velocity the local edge segments of the moving objects which is more flexible, more robust, more compact, and allows the recovery of edges that do not have a constant velocity. The method can be used in object segmentation algorithms or as a pre-filter for machine-learning-based recognition algorithms in order to improve the overall result.

Using a spatiotemporal plane to recover a moving object’s shape using Spatiotemporal Boundary Formation