28 September 2011 3D target tracking in infrared imagery by SIFT-based distance histograms
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SIFT tracking algorithm is an excellent point-based tracking algorithm, which has high tracking performance and accuracy due to its robust capability against rotation, scale change and occlusion. However, when tracking a huge 3D target in complicated real scenarios in a forward-looking infrared (FLIR) image sequence taken from an airborne moving platform, the tracked point locating in the vertical surface usually shifts away from the correct position. In this paper, we propose a novel algorithm for 3D target tracking in FLIR image sequences. Our approach uses SIFT keypoints detected in consecutive frames for point correspondence. The candidate position of the tracked point is firstly estimated by computing the affine transformation using local corresponding SIFT keypoints. Then the correct position is located via an optimal method. Euclidean distances between a candidate point and SIFT keypoints nearby are calculated and formed into a SIFT-based distance histogram. The distance histogram is defined a cost of associating each candidate point to a correct tracked point using the constraint based on the topology of each candidate point with its surrounding SIFT keypoints. Minimization of the cost is formulated as a combinatorial optimization problem. Experiments demonstrate that the proposed algorithm efficiently improves the tracking performance and accuracy.
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Ruicheng Yan, Zhiguo Cao, "3D target tracking in infrared imagery by SIFT-based distance histograms", Proc. SPIE 8185, Electro-Optical and Infrared Systems: Technology and Applications VIII, 81850R (28 September 2011); doi: 10.1117/12.897737; https://doi.org/10.1117/12.897737


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