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Towards accurate estimation for visual object tracking with multi-hierarchy feature aggregation
- Source :
- Neurocomputing. 451:252-264
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Many methods achieve the visual object tracking task with deep learning technologies. As the deep features of different levels contain various semantic information and functions, this paper presents a multi-hierarchy feature aggregation approach to tackle the specific issues in the tracking task, which consists of two aspects. On one hand, this paper integrates the features captured by the offline and online classifiers at the score level, which constructs complementary roles of these classifiers to enhance the stability of classification. Besides, the proposed offline classifier is continuously optimized with different levels of features to reinforce classification constraints. On the other hand, we design a butterfly attention module to promote the capacity of multi-hierarchy feature aggregation in the regression network, which aims to fuse and strengthen the multi-scale features by attending to their spatial information. It can capture more spatial contexts by utilizing the self-attention mechanism during the fusion procedure, and preserve the hierarchy of the features during the strengthening process. Extensive experiments on four public datasets, i.e., VOT2018, OTB100, NFS and LaSOT datasets, demonstrate the effectiveness of the proposed methods.
- Subjects :
- 0209 industrial biotechnology
Hierarchy
Computer science
business.industry
Process (engineering)
Cognitive Neuroscience
Deep learning
Stability (learning theory)
02 engineering and technology
Machine learning
computer.software_genre
Computer Science Applications
Task (project management)
020901 industrial engineering & automation
Artificial Intelligence
Video tracking
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
Classifier (UML)
computer
Spatial analysis
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 451
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........2972171ab1d4567d76d686f2e08a6b83