Multi-aircrafts tracking using spatial–temporal constraints-based intra-frame scale-invariant feature transform feature matching


Though multi-objects tracking has been improved significantly, tracking multiple aircrafts with nearly the identical appearance remains a troublesome task, especially when a vital pose changes and long-time occlusions occur within the complex setting. During this study, the authors propose a replacement multi-aircrafts tracker primarily based on a structured support vector machine (SVM) and an intra-frame scale-invariant feature remodel feature matching. The structured SVM-based mostly model adapts to the looks change well, but confuses totally different aircrafts when occlusions between aircrafts occur. To handle occlusions, an intra-frame matching methodology is applied to separate totally different aircrafts by matching points into different clusters. Moreover, to remove the mismatching caused by the cluttered background, the spatial–temporal constraint is applied to assist improve the performance of the intra-frame feature matching. As there is no dataset to judge a multi-aircrafts tracker, they select eighteen challenging videos and manually annotate the ground truth, forming the first multi-aircrafts tracking dataset. The experiments within the dataset demonstrate that the author's tracker outperforms the state-of-the-art trackers in multi-aircrafts tracking.

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