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An improved method for object instance detection based on object center estimation and convex quadrilateral verification

This paper proposes a new frame which combines kernel density estimation with convex quadrilateral verification to achieve identification and localization of object instance in zooming image. Reference object centers are first calculated based on scales, orientations, and reference vectors for all matched key points. Then the valid object center with density peak value is held. A region of interest with evaluated radius is confirmed based on the scale of the center point and row and column number of the corresponding training image. Homography matrix is computed using the matched key points within the region. Finally, four edges of the candidate training image are mapped as a quadrilateral. Only if the quadrilateral is convex that the object instance is detected. The experimental results prove that the proposed approach provides high efficiency for real-time applications with robustness.