Boundary‐aware vehicle tracking upon UAV. Issue 17 (1st August 2020)

• Record Type: Journal Article
• Authors: Han, Yuqi; Wang, Hongshuo; Zhang, Zengshuo; Wang, Wenzheng
• Is Part Of: Electronics letters. Volume 56:Issue 17(2020)
• Page Start: 873
• Page End: 876
• Publication Date: 2020-08-01
• Subjects: regression analysis -- learning (artificial intelligence) -- autonomous aerial vehicles -- object tracking -- robot vision -- filtering theory; tracking benchmark data sets -- accumulation errors -- tracking template -- boundary regressing network -- DCF tracker -- unmanned aerial vehicle platform -- accumulating drift -- precise deep learning methods -- fast DCF trackers -- viewpoint variation -- visual tracking benchmarks -- competitive performance -- DCFs -- discriminative correlation filters -- UAV -- boundary‐aware vehicle tracking; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2020.1170
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : Discriminative correlation filters (DCFs) have recently achieved competitive performance in visual tracking benchmarks. However, such trackers perform poorly when the target undergoes occlusion, viewpoint variation or other challenging attributes. To tackle these issues, in this Letter, the authors combine the fast DCF trackers with the precise deep learning methods to eliminate the accumulating drift for the vehicle tracking based on unmanned aerial vehicle platform. Specifically, the authors employ the tracking result of the DCF tracker as the input of the boundary regressing network. After judging the existence of the target in the input patch, the proposed network would estimate the boundary of the target vehicle. Furthermore, the output would be updated to the tracking template, aiming at eliminating the accumulation errors and achieving a long‐term tracking. The effectiveness of the proposed algorithm is validated through experimental comparison on widely used tracking benchmark data sets.