Research Article
An Innovative SIFT-Based Method for Rigid Video Object Recognition
Algorithm 1
Recognizinginframe(output: recognized or not,
(
),
(
); input:
,
(
)) //
to judge whether the target video object is in the frame or not.
| (1) If then | | (2) { | | (3) Set //to eliminate feature points without matching with any feature | | points in to ensure the similarity property between the recognized object views next | | (4) Set | | (5) Set recognized ← false | | (6) Set ← a threshold value //for example, 0.8 | | (7) Set ← a threshold value //for example, 0.8 | | (8) Calculate with feature points //to estimate the dimension of the target | | object in , using the Hough Transform method with feature points in | | (9) the number of feature models linked by features in | | (10) For each of feature models linked with features in do | | (11) { | | (12) Calculate with feature points in //to estimate the dimension of the target | | object in the feature model. | | (13) the number of feature keypoints in the minimum circle determined by | | (14) Calculate //to estimate the residual error which shows the degree of the similarity | | between views that and implying, denoted by and respectively | | (15) If and then | | (16) { | | (17) | | (18) } | | (19) } | | (20) If then recognized ← true | | (21) Ouput | | (22) } | | (23) Return , |
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