H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 38 Citations 8,094 170 World Ranking 5095 National Ranking 2512

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

Kris M. Kitani mainly focuses on Artificial intelligence, Computer vision, Machine learning, Feature extraction and Trajectory. The study incorporates disciplines such as Human–computer interaction and Pattern recognition in addition to Artificial intelligence. His study focuses on the intersection of Human–computer interaction and fields such as Deep learning with connections in the field of Image quality and Visualization.

His work on Object detection and Video tracking as part of general Computer vision research is frequently linked to Set and Detector, bridging the gap between disciplines. His studies examine the connections between Machine learning and genetics, as well as such issues in Optimal control, with regards to Smoothing, Kernel and Bellman equation. The various areas that Kris M. Kitani examines in his Feature extraction study include Contextual image classification, Histogram and Feature.

His most cited work include:

  • Activity forecasting (567 citations)
  • The Visual Object Tracking VOT2017 Challenge Results (285 citations)
  • Fast unsupervised ego-action learning for first-person sports videos (186 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Artificial intelligence, Computer vision, Human–computer interaction, Machine learning and Pattern recognition. His research on Artificial intelligence frequently links to adjacent areas such as Context. His studies deal with areas such as Overfitting and Benchmark as well as Context.

Much of his study explores Computer vision relationship to Wearable computer. His Human–computer interaction research incorporates themes from Transfer of learning and Personalization. His work on Leverage as part of general Machine learning research is often related to Trajectory, thus linking different fields of science.

He most often published in these fields:

  • Artificial intelligence (68.22%)
  • Computer vision (30.93%)
  • Human–computer interaction (15.68%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (68.22%)
  • Computer vision (30.93%)
  • Feature (8.47%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Artificial intelligence, Computer vision, Feature, Machine learning and Pattern recognition. His research in Artificial intelligence tackles topics such as Context which are related to areas like Benchmark. His work in Computer vision addresses subjects such as Robot, which are connected to disciplines such as Robotic arm and Eye tracking.

His research integrates issues of Artificial neural network, Feature extraction and Image description in his study of Feature. His study looks at the intersection of Pattern recognition and topics like Generative model with Regret. His Video tracking study incorporates themes from Kalman filter and Pipeline.

Between 2019 and 2021, his most popular works were:

  • Human motion trajectory prediction: a survey: (113 citations)
  • 3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (35 citations)
  • GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning (25 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Computer vision

Kris M. Kitani focuses on Artificial intelligence, Feature learning, Feature, Pattern recognition and Machine learning. His Artificial intelligence research is multidisciplinary, relying on both Context and Computer vision. His research in Context intersects with topics in Representation and Benchmark.

His Human motion and Motion prediction study, which is part of a larger body of work in Computer vision, is frequently linked to Spec# and Sequence, bridging the gap between disciplines. His study in Feature is interdisciplinary in nature, drawing from both Feature extraction and Feature vector. His work carried out in the field of Machine learning brings together such families of science as Kalman filter and State.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

The Visual Object Tracking VOT2017 Challenge Results

Matej Kristan;Ales Leonardis;Jiri Matas;Michael Felsberg.
international conference on computer vision (2017)

1389 Citations

Activity forecasting

Kris M. Kitani;Brian D. Ziebart;James Andrew Bagnell;Martial Hebert.
european conference on computer vision (2012)

570 Citations

Pixel-Level Hand Detection in Ego-centric Videos

Cheng Li;Kris M. Kitani.
computer vision and pattern recognition (2013)

303 Citations

Fast unsupervised ego-action learning for first-person sports videos

Kris M. Kitani;Takahiro Okabe;Yoichi Sato;Akihiro Sugimoto.
computer vision and pattern recognition (2011)

266 Citations

Going Deeper into First-Person Activity Recognition

Minghuang Ma;Haoqi Fan;Kris M. Kitani.
computer vision and pattern recognition (2016)

259 Citations

NavCog: a navigational cognitive assistant for the blind

Dragan Ahmetovic;Cole Gleason;Chengxiong Ruan;Kris Kitani.
human computer interaction with mobile devices and services (2016)

207 Citations

R2P2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting

Nicholas Rhinehart;Kris M. Kitani;Paul Vernaza.
european conference on computer vision (2018)

167 Citations

Learning scene-specific pedestrian detectors without real data

Hironori Hattori;Vishnu Naresh Boddeti;Kris Kitani;Takeo Kanade.
computer vision and pattern recognition (2015)

162 Citations

PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings

Nicholas Rhinehart;Rowan Mcallister;Kris Kitani;Sergey Levine.
international conference on computer vision (2019)

155 Citations

Deep Supervised Hashing with Triplet Labels

Xiaofang Wang;Yi Shi;Kris M. Kitani.
asian conference on computer vision (2016)

148 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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