Wuwei Wang
Northwestern Polytechnical University
8 Papers
17 Citations
Wuwei Wang is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Gene & Chemistry. The author has an hindex of 2, co-authored 3 publications.
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Papers
A Loosely Coupled Extended Kalman Filter Algorithm for Agricultural Scene-Based Multi-Sensor Fusion
TL;DR: Wang et al. as discussed by the authors proposed an agricultural scene-based multi-sensor fusion method via a loosely coupled extended Kalman filter algorithm to reduce interference from external environment, and the proposed method fuses inertial measurement unit (IMU), robot odometer (ODOM), global navigation and positioning system (GPS), and visual inertial odometry (VIO).
Robust Correlation Tracking in Unmanned Aerial Vehicle Videos via Deep Target-Specific Rectification Networks
Kai Zhang,Wuwei Wang,Jing Wang +2 more
TL;DR: This work proposes a robust DCF-based tracking framework via an effective pretrained rectification network for UAV-based remote sensing and proposes to finetune both the DCF module and Rectification network according to the classification confidence of the estimated result.
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Discriminative visual tracking via spatially smooth and steep correlation filters
TL;DR: A novel DCF-based tracker to precisely process the training set through a smooth and steeply decreasing function in two respects, which outperforms other state-of-the-art trackers in terms of accuracy and efficiency.
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Robust Visual Tracking Based on Adaptive Extraction and Enhancement of Correlation Filter
Wuwei Wang,Ke Zhang,Meibo Lv +2 more
TL;DR: A novel CF-based tracking method to resolve the issue of over-fitting to the recent polluted samples by dynamically and adaptively correcting the weights of learning CFs and fusing them together to promote a more robust tracking.
Multi-Level Feature Aggregation and Recursive Alignment Network for Real-Time Semantic Segmentation
Yanhua Zhang,Ke Zhang,Jing Wang,Yulin Wu,Wuwei Wang +4 more
TL;DR: A novel Multi-level Feature Aggregation and Recursive Alignment Network (MFARANet) is proposed, aiming to achieve high segmentation accuracy at real-time inference speed, and achieves a better balance between speed and accuracy than state-of-the-art real-time methods on Cityscapes and CamVid datasets.
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