Log In Sign Up

SiamAPN++: Siamese Attentional Aggregation Network for Real-Time UAV Tracking

by   Ziang Cao, et al.

Recently, the Siamese-based method has stood out from multitudinous tracking methods owing to its state-of-the-art (SOTA) performance. Nevertheless, due to various special challenges in UAV tracking, e.g., severe occlusion, and fast motion, most existing Siamese-based trackers hardly combine superior performance with high efficiency. To this concern, in this paper, a novel attentional Siamese tracker (SiamAPN++) is proposed for real-time UAV tracking. By virtue of the attention mechanism, the attentional aggregation network (AAN) is conducted with self-AAN and cross-AAN, raising the expression ability of features eventually. The former AAN aggregates and models the self-semantic interdependencies of the single feature map via spatial and channel dimensions. The latter aims to aggregate the cross-interdependencies of different semantic features including the location information of anchors. In addition, the dual features version of the anchor proposal network is proposed to raise the robustness of proposing anchors, increasing the perception ability to objects with various scales. Experiments on two well-known authoritative benchmarks are conducted, where SiamAPN++ outperforms its baseline SiamAPN and other SOTA trackers. Besides, real-world tests onboard a typical embedded platform demonstrate that SiamAPN++ achieves promising tracking results with real-time speed.


page 1

page 3

page 4

page 5

page 6


Siamese Anchor Proposal Network for High-Speed Aerial Tracking

In the domain of visual tracking, most deep learning-based trackers high...

A Twofold Siamese Network for Real-Time Object Tracking

Observing that Semantic features learned in an image classification task...

Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking

Region proposal networks (RPN) have been recently combined with the Siam...

Real-Time Siamese Multiple Object Tracker with Enhanced Proposals

Maintaining the identity of multiple objects in real-time video is a cha...

Tackling Occlusion in Siamese Tracking with Structured Dropouts

Occlusion is one of the most difficult challenges in object tracking to ...

Siamese Object Tracking for Vision-Based UAM Approaching with Pairwise Scale-Channel Attention

Although the manipulating of the unmanned aerial manipulator (UAM) has b...

Learning to Fuse Asymmetric Feature Maps in Siamese Trackers

In recent years, Siamese-based trackers have achieved promising performa...