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Found 8 papers, 2 papers with code
Deep Learning for Visual Tracking: A Comprehensive Survey
Second, popular visual tracking benchmarks and their respective properties are compared, and their evaluation metrics are summarized.
CHASE: Robust Visual Tracking via Cell-Level Differentiable Neural Architecture Search
A strong visual object tracker nowadays relies on its well-crafted modules, which typically consist of manually-designed network architectures to deliver high-quality tracking results.
A Novel Boundary Matching Algorithm for Video Temporal Error Concealment
It then uses a classic boundary matching criterion or the proposed boundary matching criterion adaptively to identify matching distortion in each boundary of candidate MB.
Effective Fusion of Deep Multitasking Representations for Robust Visual Tracking
Then, the proposed method extracts deep semantic information from a fully convolutional FEN and fuses it with the best ResNet-based feature maps to strengthen the target representation in the learning process of continuous convolution filters.
Efficient Scale Estimation Methods using Lightweight Deep Convolutional Neural Networks for Visual Tracking
In recent years, visual tracking methods that are based on discriminative correlation filters (DCF) have been very promising.
Beyond Background-Aware Correlation Filters: Adaptive Context Modeling by Hand-Crafted and Deep RGB Features for Visual Tracking
In recent years, the background-aware correlation filters have achie-ved a lot of research interest in the visual target tracking.
COMET: Context-Aware IoU-Guided Network for Small Object Tracking
To address this problem, we introduce a context-aware IoU-guided tracker (COMET) that exploits a multitask two-stream network and an offline reference proposal generation strategy.
Adaptive Exploitation of Pre-trained Deep Convolutional Neural Networks for Robust Visual Tracking
Third, the generalization of the proposed method is validated on various tracking datasets as well as CNN models with similar architectures.
