Search Results for author: Anton Milan

Found 19 papers, 6 papers with code

MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking

no code implementations15 Oct 2020 Patrick Dendorfer, Aljoša Ošep, Anton Milan, Konrad Schindler, Daniel Cremers, Ian Reid, Stefan Roth, Laura Leal-Taixé

We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data, and create a framework for the standardized evaluation of multiple object tracking methods.

Multiple Object Tracking Multiple People Tracking +2

MOT20: A benchmark for multi object tracking in crowded scenes

1 code implementation19 Mar 2020 Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, Laura Leal-Taixé

The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods.

Multi-Object Tracking Multiple Object Tracking +1

Learn to Predict Sets Using Feed-Forward Neural Networks

no code implementations30 Jan 2020 Hamid Rezatofighi, Tianyu Zhu, Roman Kaskman, Farbod T. Motlagh, Qinfeng Shi, Anton Milan, Daniel Cremers, Laura Leal-Taixé, Ian Reid

In our formulation we define a likelihood for a set distribution represented by a) two discrete distributions defining the set cardinally and permutation variables, and b) a joint distribution over set elements with a fixed cardinality.

Multi-Label Image Classification object-detection +1

PoseTrack: A Benchmark for Human Pose Estimation and Tracking

2 code implementations CVPR 2018 Mykhaylo Andriluka, Umar Iqbal, Eldar Insafutdinov, Leonid Pishchulin, Anton Milan, Juergen Gall, Bernt Schiele

In this work, we aim to further advance the state of the art by establishing "PoseTrack", a new large-scale benchmark for video-based human pose estimation and articulated tracking, and bringing together the community of researchers working on visual human analysis.

Activity Recognition Multi-Person Pose Estimation +1

PoseTrack: Joint Multi-Person Pose Estimation and Tracking

1 code implementation CVPR 2017 Umar Iqbal, Anton Milan, Juergen Gall

In this work, we introduce the challenging problem of joint multi-person pose estimation and tracking of an unknown number of persons in unconstrained videos.

Multi-Person Pose Estimation Multi-Person Pose Estimation and Tracking +1

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

8 code implementations CVPR 2017 Guosheng Lin, Anton Milan, Chunhua Shen, Ian Reid

Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation.

3D Absolute Human Pose Estimation Semantic Segmentation

Joint Probabilistic Matching Using m-Best Solutions

no code implementations CVPR 2016 Seyed Hamid Rezatofighi, Anton Milan, Zhen Zhang, Qinfeng Shi, Anthony Dick, Ian Reid

Matching between two sets of objects is typically approached by finding the object pairs that collectively maximize the joint matching score.

Person Re-Identification

Online Multi-Target Tracking Using Recurrent Neural Networks

no code implementations13 Apr 2016 Anton Milan, Seyed Hamid Rezatofighi, Anthony Dick, Ian Reid, Konrad Schindler

Here, we propose for the first time, an end-to-end learning approach for online multi-target tracking.

MOT16: A Benchmark for Multi-Object Tracking

7 code implementations2 Mar 2016 Anton Milan, Laura Leal-Taixe, Ian Reid, Stefan Roth, Konrad Schindler

Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods.

Multi-Object Tracking Multiple Object Tracking +1

Joint Probabilistic Data Association Revisited

no code implementations ICCV 2015 Seyed Hamid Rezatofighi, Anton Milan, Zhen Zhang, Qinfeng Shi, Anthony Dick, Ian Reid

In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program.

Joint Tracking and Segmentation of Multiple Targets

no code implementations CVPR 2015 Anton Milan, Laura Leal-Taixe, Konrad Schindler, Ian Reid

Tracking-by-detection has proven to be the most successful strategy to address the task of tracking multiple targets in unconstrained scenarios.

Video Segmentation Video Semantic Segmentation

MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking

2 code implementations8 Apr 2015 Laura Leal-Taixé, Anton Milan, Ian Reid, Stefan Roth, Konrad Schindler

We discuss the challenges of creating such a framework, collecting existing and new data, gathering state-of-the-art methods to be tested on the datasets, and finally creating a unified evaluation system.

3D Reconstruction Multiple Object Tracking +2

Multiple Object Tracking: A Literature Review

no code implementations26 Sep 2014 Wenhan Luo, Junliang Xing, Anton Milan, Xiaoqin Zhang, Wei Liu, Tae-Kyun Kim

We inspect the recent advances in various aspects and propose some interesting directions for future research.

Multiple Object Tracking

Detection- and Trajectory-Level Exclusion in Multiple Object Tracking

no code implementations CVPR 2013 Anton Milan, Konrad Schindler, Stefan Roth

When tracking multiple targets in crowded scenarios, modeling mutual exclusion between distinct targets becomes important at two levels: (1) in data association, each target observation should support at most one trajectory and each trajectory should be assigned at most one observation per frame; (2) in trajectory estimation, two trajectories should remain spatially separated at all times to avoid collisions.

Multiple Object Tracking

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