Tracking Non-rigid Object Based on Region Relativity in Global Active Video
Presents an OT-GAV model for automatic detecting and tracking non-rigid objects in global active video.The model computes region matching between adjoining frames based on RDM algorithm.Q-learning and K-S statistic are used to optimize matching results so that the region motion vectors are precise in a certain extent.Then motion modality difference between foreground and background,consistency of region dynamic texture,and region integrality during object moving are gradually utilized to realize object regions detecting and combination.The experiment proves this model and correlative algorithms could automatically detect foreground attentive objects,obtain precise edge information and effectively track in outdoor or indoor circumstance.Besides,this model is able to resolve empty-hole problem during object tracking.