Most of the correlation filter-based trackers learn a 1D correlation filter for scale search. The scale searching space of 1D scale filter is discrete, which results in high computation complexity and low scale estimation accuracy. In this paper, we present an interpolation model for accurate scale estimation for correlation filter-based tracking. Experiments are performed on selected images from the OTB (Object Tracking Benchmark) datasets. Compared to the standard 1D correlation filter for scale search, our approach has better average distance precision and overlap precision. Additionally, the center distance error is even less than half of the standard 1D correlation filter. Meanwhile, our method is computationally efficient, operating at a 20.7% higher frame rate.
Small satellites have a bright prospect in space exploration. One of the bottlenecks in developing small satellites is precise attitude estimation. Therefore, a relative attitude estimation method for small satellite is proposed using the stellar images captured by stellar camera. Essentially, the proposed method determines satellites relative attitude variation by infinite homography, which provides constraints on attitude variation of satellite camera. Homography between stellar images induced by stars is used to approximate the infinite homography which is obtained by matching two stellar images captured by satellite camera at different time. The specific procedure of the proposed method is as follows: First, star points features are extracted for matching by segmentation and non-maxima suppression. Second, star descriptors are constructed in a far larger local region for star points based on Speeded Up Robust Features (SURF) descriptors. Third, nearest neighbor method is used to get candidate correspondent star points and then outliers are rejected with RANSAC based strategy. Finally, relative attitude variation between two stellar images is solved with matched star points using regularized least square method. In order to verify the feasibility of the proposed method, experiments are carried out with simulated stellar images and digital simulation. Simulated stellar image matching experiment shows stars points matching method can get abundant of correspondent stars and the errors are below 1 pixel. Digital simulation shows that the accuracy of relative attitude estimation reaches 0.01° with about 0.5 pixels star location errors. Experiment results indicate that the proposed method is feasible and can precisely determine satellite relative attitude change.
To realize stable wide-baseline matching for structured scenes with low texture regions, a new matching method based on line intersection features (LIFS) is proposed, which combines the robustness of line feature and distinctiveness of keypoint’s descriptor. First, detect lines and compute line intersections. Second, line intersections from perspective projection of parallel lines or skew lines are filtered by parallel lines clustering and coplanar constraint which increases the stability and accuracy of line intersections. Third, local non maxima suppression is used to limit the intersections close to each other. Fourth, feature scales are computed for LIFS by simply utilizing the geometry distribution of intersections and endpoints of intersection lines. Finally, SURF descriptors are computed for LIFS in the computed scales and thus scale and rotation invariance is achieved. Experiment results show that compared with traditional matching method based on local features, the proposed method is more robust to image noise and illumination change. Besides, the proposed method has invariance to scale and rotation change and a certain degree of viewpoint change, providing an effective wide baseline matching method for images of structured scenes.