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

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MOT20: A benchmark for multi object tracking in crowded scenes

Standardized benchmarks are crucial for the majority of computer vision ...
03/19/2020 ∙ by Patrick Dendorfer, et al. ∙ 17 ∙ share

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Learn to Predict Sets Using Feed-Forward Neural Networks

This paper addresses the task of set prediction using deep feed-forward ...
01/30/2020 ∙ by Hamid Rezatofighi, et al. ∙ 30 ∙ share

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

Standardized benchmarks are crucial for the majority of computer vision ...
06/10/2019 ∙ by Patrick Dendorfer, et al. ∙ 8 ∙ share

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

Autonomous robotic manipulation in clutter is challenging. A large varie...
10/01/2018 ∙ by Max Schwarz, et al. ∙ 20 ∙ share

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PoseTrack: A Benchmark for Human Pose Estimation and Tracking

Human poses and motions are important cues for analysis of videos with p...
10/27/2017 ∙ by Mykhaylo Andriluka, et al. ∙ 0 ∙ share

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Joint Learning of Set Cardinality and State Distribution

We present a novel approach for learning to predict sets using deep lear...
09/13/2017 ∙ by S. Hamid Rezatofighi, et al. ∙ 0 ∙ share

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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 ...
04/10/2017 ∙ by Laura Leal-Taixé, et al. ∙ 0 ∙ share

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DeepSetNet: Predicting Sets with Deep Neural Networks

This paper addresses the task of set prediction using deep learning. Thi...
11/28/2016 ∙ by S. Hamid Rezatofighi, et al. ∙ 0 ∙ share

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PoseTrack: Joint Multi-Person Pose Estimation and Tracking

In this work, we introduce the challenging problem of joint multi-person...
11/23/2016 ∙ by Umar Iqbal, et al. ∙ 0 ∙ share

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

Recently, very deep convolutional neural networks (CNNs) have shown outs...
11/20/2016 ∙ by Guosheng Lin, et al. ∙ 0 ∙ share

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Online Multi-Target Tracking Using Recurrent Neural Networks

We present a novel approach to online multi-target tracking based on rec...
04/13/2016 ∙ by Anton Milan, et al. ∙ 0 ∙ share

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MOT16: A Benchmark for Multi-Object Tracking

Standardized benchmarks are crucial for the majority of computer vision ...
03/02/2016 ∙ by Anton Milan, et al. ∙ 0 ∙ share

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MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking

In the recent past, the computer vision community has developed centrali...
04/08/2015 ∙ by Laura Leal-Taixé, et al. ∙ 0 ∙ share

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Multiple Object Tracking: A Literature Review

Multiple Object Tracking (MOT) is an important computer vision problem w...
09/26/2014 ∙ by Wenhan Luo, et al. ∙ 0 ∙ share

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