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Our aim is to estimate the perspective-effected geometric distortion of ascene from a video feed. In contrast to all previous work we wish to achievethis using from low-level, spatio-temporally local motion features used incommercial semi-automatic surveillance systems. We: (i) describe a densealgorithm which uses motion features to estimate the perspective distortion ateach image locus and then polls all such local estimates to arrive at theglobally best estimate, (ii) present an alternative coarse algorithm whichsubdivides the image frame into blocks, and uses motion features to deriveblock-specific motion characteristics and constrain the relationships betweenthese characteristics, with the perspective estimate emerging as a result of aglobal optimization scheme, and (iii) report the results of an evaluation usingnine large sets acquired using existing close-circuit television (CCTV)cameras. Our findings demonstrate that both of the proposed methods aresuccessful, their accuracy matching that of human labelling using completevisual data.

Viewpoint distortion compensation in practical surveillance systems