A Novel Target-Aware Dual Matching and Compensatory Segmentation Tracker for Aerial Videos

Abstract

Object tracking in aerial scenes is widely used in intelligent transportation and national defense security. Template-based matching trackers currently dominate tracking pattern due to their remarkable performance, but are confined to rectangular bounding box tracking and the lack of effective transformation models, which lead to their low position accuracy in aerial scenes of special difficulties, such as low target resolution, large field of view, and multiple angle changes. To address this issue, we develop a novel target-aware dual matching and compensatory segmentation (TMCS) tracker for aerial videos, which transforms visual object tracking into a video object segmentation problem. Because deep features of the offline trained network are less discriminative in representing these targets of arbitrary form, we develop a regression loss for selecting the most representative convolutional filters to learn salient features of aerial target. In contrast to general trackers, the proposed approach designs the dual match mechanism, which matches the target activation features with the foreground and background features of the target template to generate geometric invariant models, rather than offline remembering the appearance of the target. In addition, most tracking benchmarks require marking the object position as a rectangular bounding box. In this article, an online fast fitting method is proposed to interpret the output form of the target as a rotating rectangular bounding box that is more suitable for the target, which improves object location accuracy. Extensive results on the challenging UAV123 dataset show that our method achieves comparable performance, which basically meets the aerial target tracking accuracy and real-time requirements.