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.
no code implementations • 26 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.
2 code implementations • 8 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.
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.
1 code implementation • 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.
8 code implementations • 2 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.
no code implementations • 13 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.
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.
13 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.
Ranked #13 on Semantic Segmentation on Trans10K
2 code implementations • 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.
Ranked #1 on Pose Tracking on Multi-Person PoseTrack
Multi-Person Pose Estimation Multi-Person Pose Estimation and Tracking +1
no code implementations • ICCV 2017 • S. Hamid Rezatofighi, Vijay Kumar B G, Anton Milan, Ehsan Abbasnejad, Anthony Dick, Ian Reid
This paper addresses the task of set prediction using deep learning.
no code implementations • 10 Apr 2017 • Laura Leal-Taixé, Anton Milan, Konrad Schindler, Daniel Cremers, Ian Reid, Stefan Roth
Standardized benchmarks are crucial for the majority of computer vision applications.
no code implementations • 13 Sep 2017 • S. Hamid Rezatofighi, Anton Milan, Qinfeng Shi, Anthony Dick, Ian Reid
We present a novel approach for learning to predict sets using deep learning.
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.
Ranked #3 on Multi-Person Pose Estimation on PoseTrack2017
no code implementations • 1 Oct 2018 • Max Schwarz, Anton Milan, Arul Selvam Periyasamy, Sven Behnke
Autonomous robotic manipulation in clutter is challenging.
no code implementations • 10 Jun 2019 • Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, Laura Leal-Taixe
Standardized benchmarks are crucial for the majority of computer vision applications.
no code implementations • 30 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.
1 code implementation • 19 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 with Transformer +2
no code implementations • 15 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.