What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Martin Danelljan focuses on Artificial intelligence, Video tracking, Computer vision, Eye tracking and Discriminative model.
His Artificial intelligence research incorporates elements of Machine learning and Pattern recognition.
His work carried out in the field of Pattern recognition brings together such families of science as Color normalization, Color quantization, Demosaicing and Color image.
His work on Correlation filter, Image and Color balance as part of general Computer vision study is frequently linked to Source code and Scale estimation, therefore connecting diverse disciplines of science.
Martin Danelljan frequently studies issues relating to Color histogram and Eye tracking.
His Discriminative model research is multidisciplinary, incorporating elements of Feature extraction, Robustness and Convolution.
His most cited work include:
- Accurate scale estimation for robust visual tracking (1343 citations)
- Learning Spatially Regularized Correlation Filters for Visual Tracking (1024 citations)
- ECO: Efficient Convolution Operators for Tracking (972 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Computer vision, Eye tracking, Discriminative model and Pattern recognition.
His Machine learning research extends to Artificial intelligence, which is thematically connected.
The Eye tracking study combines topics in areas such as Visualization and BitTorrent tracker.
His research integrates issues of Filter, Feature, Inference, Active appearance model and Convolution in his study of Discriminative model.
His work on Convolutional neural network and Classifier is typically connected to Correlation as part of general Pattern recognition study, connecting several disciplines of science.
In his research on the topic of Robustness, Bayesian probability and Uncertainty quantification is strongly related with Artificial neural network.
He most often published in these fields:
- Artificial intelligence (91.78%)
- Computer vision (43.84%)
- Eye tracking (41.10%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (91.78%)
- Segmentation (16.44%)
- Computer vision (43.84%)
In recent papers he was focusing on the following fields of study:
Martin Danelljan mostly deals with Artificial intelligence, Segmentation, Computer vision, Machine learning and Frame.
His research on Artificial intelligence frequently connects to adjacent areas such as Pattern recognition.
His study in the field of Unsupervised learning is also linked to topics like Scaling.
His Computer vision study combines topics in areas such as Artificial neural network and Deep learning.
His Deep learning research includes elements of Object and Minimum bounding box.
His research investigates the connection between BitTorrent tracker and topics such as Video tracking that intersect with issues in RGB color model.
Between 2019 and 2021, his most popular works were:
- Probabilistic Regression for Visual Tracking (44 citations)
- Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision (33 citations)
- The Eighth Visual Object Tracking VOT2020 Challenge Results (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Martin Danelljan focuses on Artificial intelligence, Machine learning, Eye tracking, Divergence and Probabilistic logic.
Martin Danelljan combines topics linked to Computer vision with his work on Artificial intelligence.
Martin Danelljan has researched Machine learning in several fields, including Representation and Noise.
His biological study spans a wide range of topics, including RGB color model, Ground truth and Video tracking.
His Ground truth study frequently involves adjacent topics like Segmentation.
Martin Danelljan combines subjects such as Uncertainty quantification, Artificial neural network, Deep learning and Bayesian probability with his study of Robustness.
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