What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Weiming Hu mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Video tracking and Feature extraction.
His research links Machine learning with Artificial intelligence.
Weiming Hu has researched Pattern recognition in several fields, including Algorithm, Covariance matrix and Cluster analysis.
His studies deal with areas such as Visualization, Discriminative model, Eye tracking and Frame rate as well as Video tracking.
His work carried out in the field of Feature extraction brings together such families of science as Classifier, Contextual image classification and Metric.
His studies in Biometrics integrate themes in fields like Background subtraction, Shape analysis and Machine vision.
His most cited work include:
- A survey on visual surveillance of object motion and behaviors (1920 citations)
- Recent developments in human motion analysis (924 citations)
- Silhouette analysis-based gait recognition for human identification (920 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Video tracking and Feature extraction.
His work in Artificial intelligence addresses issues such as Machine learning, which are connected to fields such as Benchmark.
His Pattern recognition research integrates issues from Histogram, Feature, Data mining and Subspace topology.
His work is connected to Tracking, Pixel, Eye tracking, Object and Active appearance model, as a part of Computer vision.
The Video tracking study combines topics in areas such as Particle filter, Segmentation, Graph embedding and Object detection.
His Feature extraction research incorporates themes from Cognitive neuroscience of visual object recognition, Classifier, Feature learning, Visualization and Convolutional neural network.
He most often published in these fields:
- Artificial intelligence (89.30%)
- Pattern recognition (49.54%)
- Computer vision (47.71%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (89.30%)
- Computer vision (47.71%)
- Video tracking (22.32%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Computer vision, Video tracking, Pattern recognition and Feature extraction.
His Artificial intelligence study is mostly concerned with Eye tracking, Segmentation, Object, Discriminative model and Robustness.
In general Computer vision, his work in BitTorrent tracker, Tracking, Object detection and RGB color model is often linked to Source code linking many areas of study.
His Video tracking study combines topics from a wide range of disciplines, such as Embedding and Frame rate.
His work on Convolutional neural network as part of general Pattern recognition study is frequently connected to Construct, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Feature extraction research focuses on Machine learning and how it relates to Closed captioning.
Between 2017 and 2021, his most popular works were:
- Fast Online Object Tracking and Segmentation: A Unifying Approach (367 citations)
- Distractor-aware Siamese Networks for Visual Object Tracking (366 citations)
- The sixth visual object tracking VOT2018 challenge results (299 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Artificial intelligence, Computer vision, Video tracking, Object and Pattern recognition are his primary areas of study.
His Artificial intelligence study focuses mostly on Eye tracking, Feature learning, Segmentation, Robustness and Discriminative model.
His Robustness study which covers Training set that intersects with Machine learning.
His research in Video tracking intersects with topics in Visualization and Frame rate.
His work in Object covers topics such as Deep learning which are related to areas like Feature vector, Categorization and Theoretical computer science.
His work on Sparse approximation as part of general Pattern recognition research is often related to Construct, thus linking different fields of science.
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