Asset Details
Do you wish to reserve the book?
SiamADT: Siamese Attention and Deformable Features Fusion Network for Visual Object Tracking
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
SiamADT: Siamese Attention and Deformable Features Fusion Network for Visual Object Tracking
Do you wish to request the book?
SiamADT: Siamese Attention and Deformable Features Fusion Network for Visual Object Tracking
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
SiamADT: Siamese Attention and Deformable Features Fusion Network for Visual Object Tracking
2023
Overview
To date, existing Siamese-based trackers have achieved excellent performance. However, in some complex scenarios, using deep convolutional layers alone can not effectively capture powerful representative features. To solve this problem, we propose a Siamese Attention and Deformable features fusion network for visual object Tracking (SiamADT). The proposed SiamADT consists of three modules: a Siamese attention network module for attention feature extraction, a deformable features fusion module, and a classification-regression module for bounding box prediction. Our framework uses ResNet-50 as the backbone for anchor-free tracking. Without tricky anchor hyperparameters tuning and manual intervention, SiamADT is more flexible and versatile. We conduct extensive experiments on four challenging benchmark datasets. The results demonstrate that SiamADT achieves competitive performance among state-of-the-art methods, with real-time speed—30 frames per second.
Publisher
Springer US,Springer Nature B.V
