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

Although multi-objects tracking has been improved significantly, tracking multiple aircrafts with nearly the same appearance remains a difficult task, especially when a significant pose changes and long-time occlusions occur in the complex environment. In this study, the authors propose a new multi-aircrafts tracker based on a structured support vector machine (SVM) and an intra-frame scale-invariant feature transform feature matching. The structured SVM-based model adapts to the appearance change well, but confuses different aircrafts when occlusions between aircrafts occur. To handle occlusions, an intra-frame matching method is applied to separate 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 help improve the performance of the intra-frame feature matching. As there is no dataset to evaluate a multi-aircrafts tracker, they select eighteen challenging videos and manually annotate the ground truth, forming the first multi-aircrafts tracking dataset. The experiments in the dataset demonstrate that the author's tracker outperforms the state-of-the-art trackers in multi-aircrafts tracking.