His main research concerns Artificial intelligence, Computer vision, Object detection, Video tracking and Pattern recognition. Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Machine learning. The study incorporates disciplines such as Robustness and Laser scanning in addition to Computer vision.
His research in Object detection intersects with topics in Pedestrian detection and Detector. Konrad Schindler focuses mostly in the field of Pattern recognition, narrowing it down to topics relating to Data modeling and, in certain cases, Markov random field, Mutual exclusion and Conditional random field. His study in Object is interdisciplinary in nature, drawing from both Tracking and Data mining.
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Object and Point cloud. His study looks at the relationship between Artificial intelligence and topics such as Machine learning, which overlap with Contextual image classification. His work on Video tracking, Tracking, Motion estimation and Pixel as part of general Computer vision research is frequently linked to Set, thereby connecting diverse disciplines of science.
His research investigates the link between Video tracking and topics such as Benchmark that cross with problems in Data mining. In general Pattern recognition study, his work on Classifier and Feature extraction often relates to the realm of Matching, thereby connecting several areas of interest. His work deals with themes such as Artificial neural network and Training set, which intersect with Object.
Konrad Schindler mainly focuses on Artificial intelligence, Computer vision, Object, Machine learning and Point cloud. As part of his studies on Artificial intelligence, Konrad Schindler often connects relevant subjects like Pattern recognition. His study on Computer vision is mostly dedicated to connecting different topics, such as Rolling shutter.
His work deals with themes such as Monocular, Training set, Artificial neural network, Robot and Focus, which intersect with Object. His Machine learning research also works with subjects such as
Pooling that intertwine with fields like Contextual image classification and Discriminative model,
Multispectral image and related RGB color model, STREAMS and Pixel. His Point cloud study also includes fields such as
Voxel which is related to area like Perception,
Grid that connect with fields like Feature extraction.
His scientific interests lie mostly in Artificial intelligence, Computer vision, Object, Machine learning and Benchmark. His study in Monocular, Image, Deep learning, Convolutional neural network and Ground truth are all subfields of Artificial intelligence. The Segmentation and Distortion research Konrad Schindler does as part of his general Computer vision study is frequently linked to other disciplines of science, such as Set and Full model, therefore creating a link between diverse domains of science.
As a part of the same scientific family, Konrad Schindler mostly works in the field of Object, focusing on Artificial neural network and, on occasion, Image formation, Perspective, Superresolution and Texture mapping. His Machine learning research incorporates elements of Flood mitigation, Small set and Social media. The concepts of his Benchmark study are interwoven with issues in Stereo reconstruction, Video tracking, Surface, Residual and Pattern recognition.
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You'll never walk alone: Modeling social behavior for multi-target tracking
S. Pellegrini;A. Ess;K. Schindler;L. van Gool.
international conference on computer vision (2009)
MOT16: A Benchmark for Multi-Object Tracking
Anton Milan;Laura Leal-Taixé;Ian D. Reid;Stefan Roth.
arXiv: Computer Vision and Pattern Recognition (2016)
A mobile vision system for robust multi-person tracking
A. Ess;B. Leibe;K. Schindler;L. Van Gool.
computer vision and pattern recognition (2008)
Action snippets: How many frames does human action recognition require?
K. Schindler;L. van Gool.
computer vision and pattern recognition (2008)
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
Laura Leal-Taixé;Anton Milan;Ian D. Reid;Stefan Roth.
arXiv: Computer Vision and Pattern Recognition (2015)
New features and insights for pedestrian detection
Stefan Walk;Nikodem Majer;Konrad Schindler;Bernt Schiele.
computer vision and pattern recognition (2010)
Continuous Energy Minimization for Multitarget Tracking
Anton Milan;Stefan Roth;Konrad Schindler.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2014)
Online multi-target tracking using recurrent neural networks
Anton Milan;S. Hamid Rezatofighi;Anthony Dick;Ian Reid.
national conference on artificial intelligence (2017)
Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection
Dimitrios Marmanis;Dimitrios Marmanis;Konrad Schindler;Jan Dirk Wegner;Silvano Galliani.
Isprs Journal of Photogrammetry and Remote Sensing (2018)
Discrete-continuous optimization for multi-target tracking
Anton Andriyenko;Konrad Schindler;Stefan Roth.
computer vision and pattern recognition (2012)
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