Adaptive Fusion

2019 ◽  
pp. 587-606
Author(s):  
Vincent Nimier ◽  
Kaouthar Benameur
Keyword(s):  
Adaptive Fusion

An Adaptive Fusion Successive Cancellation List Decoder for Polar Codes with Cyclic Redundancy Check

2020 ◽  
Vol E103.B (1) ◽  
pp. 43-51 ◽  
Author(s):  
Yuhuan WANG ◽  
Hang YIN ◽  
Zhanxin YANG ◽  
Yansong LV ◽  
Lu SI ◽  
...  
Keyword(s):  
Polar Codes ◽  
Cyclic Redundancy Check ◽  
Successive Cancellation ◽  
Adaptive Fusion ◽  
List Decoder

scholarly journals A Hybrid Framework for Mitigating Heading Drift for a Wearable Pedestrian Navigation System through Adaptive Fusion of Inertial and Magnetic Measurements

2021 ◽  
Vol 11 (4) ◽  
pp. 1902
Author(s):  
Liqiang Zhang ◽  
Yu Liu ◽  
Jinglin Sun
Keyword(s):  
Magnetic Measurements ◽  
Inertial Sensor ◽  
Angular Rate ◽  
Human Motion ◽  
Navigation Systems ◽  
Pedestrian Navigation ◽  
Hybrid Framework ◽  
Outdoor Environments ◽  
Adaptive Fusion ◽  
Indoor And Outdoor Environments

Pedestrian navigation systems could serve as a good supplement for other navigation methods or for extending navigation into areas where other navigation systems are invalid. Due to the accumulation of inertial sensing errors, foot-mounted inertial-sensor-based pedestrian navigation systems (PNSs) suffer from drift, especially heading drift. To mitigate heading drift, considering the complexity of human motion and the environment, we introduce a novel hybrid framework that integrates a foot-state classifier that triggers the zero-velocity update (ZUPT) algorithm, zero-angular-rate update (ZARU) algorithm, and a state lock, a magnetic disturbance detector, a human-motion-classifier-aided adaptive fusion module (AFM) that outputs an adaptive heading error measurement by fusing heuristic and magnetic algorithms rather than simply switching them, and an error-state Kalman filter (ESKF) that estimates the optimal systematic error. The validation datasets include a Vicon loop dataset that spans 324.3 m in a single room for approximately 300 s and challenging walking datasets that cover large indoor and outdoor environments with a total distance of 12.98 km. A total of five different frameworks with different heading drift correction methods, including the proposed framework, were validated on these datasets, which demonstrated that our proposed ZUPT–ZARU–AFM–ESKF-aided PNS outperforms other frameworks and clearly mitigates heading drift.

scholarly journals Adaptive fusion of particle filtering and spatio-temporal motion energy for human tracking

2014 ◽  
Vol 47 (11) ◽  
pp. 3552-3567 ◽  
Author(s):  
Huiyu Zhou ◽  
Minrui Fei ◽  
Abdul Sadka ◽  
Yi Zhang ◽  
Xuelong Li
Keyword(s):  
Particle Filtering ◽  
Human Tracking ◽  
Motion Energy ◽  
Adaptive Fusion ◽  
Spatio Temporal

Source camera identification based on content-adaptive fusion residual networks

2019 ◽  
Vol 119 ◽  
pp. 195-204 ◽  
Author(s):  
Pengpeng Yang ◽  
Rongrong Ni ◽  
Yao Zhao ◽  
Wei Zhao
Keyword(s):  
Source Camera Identification ◽  
Camera Identification ◽  
Adaptive Fusion ◽  
Content Adaptive

scholarly journals Correlation Tracking via Self-Adaptive Fusion of Multiple Features

Information ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 241 ◽  
Author(s):  
Zhi Chen ◽  
Peizhong Liu ◽  
Yongzhao Du ◽  
Yanmin Luo ◽  
Wancheng Zhang
Keyword(s):  
State Of The Art ◽  
Correlation Filter ◽  
Multiple Features ◽  
Multi Scale ◽  
Tracking Algorithms ◽  
Model Update ◽  
Adaptive Fusion ◽  
Tracking Model ◽  
Update Strategy ◽  
Self Adaptive

Correlation filter (CF) based tracking algorithms have shown excellent performance in comparison to most state-of-the-art algorithms on the object tracking benchmark (OTB). Nonetheless, most CF based tracking algorithms only consider limited single channel feature, and the tracking model always updated from frame-by-frame. It will generate some erroneous information when the target objects undergo sophisticated scenario changes, such as background clutter, occlusion, out-of-view, and so forth. Long-term accumulation of erroneous model updating will cause tracking drift. In order to address problems that are mentioned above, in this paper, we propose a robust multi-scale correlation filter tracking algorithm via self-adaptive fusion of multiple features. First, we fuse powerful multiple features including histogram of oriented gradients (HOG), color name (CN), and histogram of local intensities (HI) in the response layer. The weights assigned according to the proportion of response scores that are generated by each feature, which achieve self-adaptive fusion of multiple features for preferable feature representation. In the meantime the efficient model update strategy is proposed, which is performed by exploiting a pre-defined response threshold as discriminative condition for updating tracking model. In addition, we introduce an accurate multi-scale estimation method integrate with the model update strategy, which further improves the scale variation adaptability. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracker performs superiorly against the state-of-the-art CF based methods.

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