Dynamic Weighting based Active Curve Propagation Method for Video Object Selection

Improving video user experience is an essential task allowing video based algorithms and systems to be more user-friendly. This paper addresses the problem of video object selection by introducing a new interactive framework based on the minimization of the Active Curve energy. Prior assumption and supervised learning can be used to segment images using both color and morphological information. To deal with the segmentation of arbitrary high level object, user interaction is needed to avoid the semantic gap. Hard constraints such scribbles can be drown by user on the first video frame, to roughly mark the object of interest, and there are then automatically propagated to designate the same object in the remainder of the sequence. The resulting scribbles can be used as hard constraints to achieve the whole segmentation process. The active curve model is adapted and new forces are included to govern the curves evolution frame by frame. A spatiotemporal optimization is used to ensure a coherent propagation. To avoid weight definition problem, as in classical active curve based algorithms, a new concept of dynamically adjusted weighting is introduced in order to improve the robustness of our curve propagation.