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

Abstract

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.

Keywords

• spatial-temporal constraint
• intraframe scale-invariant feature transform feature matching
• multiobject tracking
• multiple aircraft tracking
• occlusion
• structured support vector machine