SiamHAS: Siamese Tracker with Hierarchical Attention Strategy for Aerial Tracking

Abstract

For the Siamese network-based trackers utilizing modern deep feature extraction networks without taking full advantage of the different levels of features, tracking drift is prone to occur in aerial scenarios, such as target occlusion, scale variation, and low-resolution target tracking. Additionally, the accuracy is low in challenging scenarios of visual tracking, which is due to the imperfect utilization of features. To improve the performance of the existing Siamese tracker in the above-mentioned challenging scenes, we propose a Siamese tracker based on Transformer multi-level feature enhancement with a hierarchical attention strategy. The saliency of the extracted features is enhanced by the process of Transformer Multi-level Enhancement; the application of the hierarchical attention strategy makes the tracker adaptively notice the target region information and improve the tracking performance in challenging aerial scenarios. Meanwhile, we conducted extensive experiments and qualitative or quantitative discussions on UVA123, UAV20L, and OTB100 datasets. Finally, the experimental results show that our SiamHAS performs favorably against several state-of-the-art trackers in these challenging scenarios.

SiamMAN: Siamese Multi-Phase Aware Network for Real-Time Unmanned Aerial Vehicle Tracking

“…We compare our proposed model with plenty of state-of-the-art methods in the field of aerial tracking, including SiamCAR [16], SiamBAN [17], SiamTPN [18], HiFT [19], SiamRPN [25], DaSiamRPN [27], SiamRPN++ [28], SiamFC++ [29], SiamAPN [30], SiamAPN++ [31], SiamHAS [38], SE-SiamFC [39], SGD-ViT [40], SiamMask [47], Ocean [48], ATOM [49], SiamDW [50], MDNet [51], SiamAttn [52], ECO [53], Neighbor Track [54], TC-Track [55], ARTrack [56], MixFormer [57] and several recent traditional SOTA trackers TMCS [58] and CFIT [59]. For a fair comparison, all the tracking results are provided by the authors or achieved using available codes.…”

“…Later, ViT [36] and MobileViT [37] were the first to introduce a more effective transformer architecture into computer vision tasks, breaking the limitation that CNNs can only acquire local information and ignore global information, thus enabling the modeling of dependencies between distant pixels. SiamHAS [38] proposed a tracking method with a hierarchical attention strategy that makes better use of the global relevance of features through the introduction of a multi-layer attention mechanism to achieve more accurate tracking. SE-SiamFC [39] used a scale model to break the limits of translational invariance and enhance the accuracy of the output prediction frame results of the classification regression network.…”