.png){kind=icon}
Return
GRM: Gaussian response module for visual tracking
.png){kind=icon}
PRE
AI
DOI：10.1016/j.displa.2023.102509.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Online Discriminative Correlation Filters have excellent performance in visual object tracking. It always divides the tracking network as a classification network and a regression network, which makes the regression network lack classification information and makes it hard to leverage its advantages. To address this problem, we propose the ATOM_GRM tracker to append the classification information to the network. Specifically, we design static and dynamic Gaussian response modules to encode the raw target position from classifier to regression network by multistage. In Particular, the parameters and fusion method of two Gaussian response modules is designed in different ways according to their location in the network. Moreover, we propose the ratio of height and width of the bounding box instead of intersection of union as the predictor, and two auxiliary training heads are used in the regression network. It makes the regression network better distinguish bounding boxes. Extensive experi-ments conducted on four public benchmarks, i.e., OTB100, GOT10k, LaSOT, Trackingnet, demonstrate the effectiveness of the proposed method. The proposed ATOM_GRM achieves 0.556 ? 0.596 AO compared with the baseline ATOM on GOT10k.
Keywords:
Visual object tracking
Discriminative correlation filter
The ratio of height and width
Gaussian response modules
Journal
IF:
3.4
Papers:
2.3K
Citations:
3.2K
Organization
No organization information available
Cited Papers
.png){kind=icon}
Young age and tobacco use are predictors of lower medication adherence in inflammatory bowel disease
PREAI
ShareSaveGeriatric Assessments and Medication Therapy Management Approaches in Elderly Oncology Patients
PREAI
ShareSave
View more