Anton Milan

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The University of Adelaide

Applied Scientist, Research in machine learning, computer vision and robotics at the Amazon Research and Development Center in Berlin

research

∙ 10/15/2020

MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking

Standardized benchmarks have been crucial in pushing the performance of ...

Patrick Dendorfer, et al. ∙

research

∙ 03/19/2020

MOT20: A benchmark for multi object tracking in crowded scenes

Standardized benchmarks are crucial for the majority of computer vision ...

Patrick Dendorfer, et al. ∙

research

∙ 01/30/2020

Learn to Predict Sets Using Feed-Forward Neural Networks

This paper addresses the task of set prediction using deep feed-forward ...

Hamid Rezatofighi, et al. ∙

research

∙ 06/10/2019

CVPR19 Tracking and Detection Challenge: How crowded can it get?

Standardized benchmarks are crucial for the majority of computer vision ...

Patrick Dendorfer, et al. ∙

research

∙ 10/01/2018

RGB-D Object Detection and Semantic Segmentation for Autonomous Manipulation in Clutter

Autonomous robotic manipulation in clutter is challenging. A large varie...

Max Schwarz, et al. ∙

research

∙ 10/27/2017

PoseTrack: A Benchmark for Human Pose Estimation and Tracking

Human poses and motions are important cues for analysis of videos with p...

Mykhaylo Andriluka, et al. ∙

research

∙ 09/13/2017

Joint Learning of Set Cardinality and State Distribution

We present a novel approach for learning to predict sets using deep lear...

S. Hamid Rezatofighi, et al. ∙

research

∙ 04/10/2017

Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking

Standardized benchmarks are crucial for the majority of computer vision ...

Laura Leal-Taixé, et al. ∙

research

∙ 11/28/2016

DeepSetNet: Predicting Sets with Deep Neural Networks

This paper addresses the task of set prediction using deep learning. Thi...

S. Hamid Rezatofighi, et al. ∙

research

∙ 11/23/2016

PoseTrack: Joint Multi-Person Pose Estimation and Tracking

In this work, we introduce the challenging problem of joint multi-person...

Umar Iqbal, et al. ∙

research

∙ 11/20/2016

RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

Recently, very deep convolutional neural networks (CNNs) have shown outs...

Guosheng Lin, et al. ∙

research

∙ 04/13/2016

Online Multi-Target Tracking Using Recurrent Neural Networks

We present a novel approach to online multi-target tracking based on rec...

Anton Milan, et al. ∙

research

∙ 03/02/2016

MOT16: A Benchmark for Multi-Object Tracking

Standardized benchmarks are crucial for the majority of computer vision ...

Anton Milan, et al. ∙

research

∙ 04/08/2015

MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking

In the recent past, the computer vision community has developed centrali...

Laura Leal-Taixé, et al. ∙

research

∙ 09/26/2014

Multiple Object Tracking: A Literature Review

Multiple Object Tracking (MOT) is an important computer vision problem w...

Wenhan Luo, et al. ∙

Anton Milan

Featured Co-authors

Wei Liu

Chunhua Shen

Daniel Cremers

Bernt Schiele

Sven Behnke

Ian Reid

Guosheng Lin

Juergen Gall

Konrad Schindler

Laura Leal-Taixé

Tae-Kyun Kim

MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking

MOT20: A benchmark for multi object tracking in crowded scenes

Learn to Predict Sets Using Feed-Forward Neural Networks

CVPR19 Tracking and Detection Challenge: How crowded can it get?

RGB-D Object Detection and Semantic Segmentation for Autonomous Manipulation in Clutter

PoseTrack: A Benchmark for Human Pose Estimation and Tracking

Joint Learning of Set Cardinality and State Distribution

Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking

DeepSetNet: Predicting Sets with Deep Neural Networks

PoseTrack: Joint Multi-Person Pose Estimation and Tracking

RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

Online Multi-Target Tracking Using Recurrent Neural Networks

MOT16: A Benchmark for Multi-Object Tracking

MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking

Multiple Object Tracking: A Literature Review