Learning a neural solver for multiple object tracking

G Brasó, L Leal-Taixé - … of the IEEE/CVF conference on …, 2020 - openaccess.thecvf.com
Graphs offer a natural way to formulate Multiple Object Tracking (MOT) within the tracking-by-
detection paradigm. However, they also introduce a major challenge for learning methods …

Detection and tracking meet drones challenge

P Zhu, L Wen, D Du, X Bian, H Fan… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Drones, or general UAVs, equipped with cameras have been fast deployed with a wide
range of applications, including agriculture, aerial photography, and surveillance …

Virtual worlds as proxy for multi-object tracking analysis

A Gaidon, Q Wang, Y Cabon, E Vig - Proceedings of the IEEE …, 2016 - cv-foundation.org
Modern computer vision algorithms typically require expensive data acquisition and
accurate manual labeling. In this work, we instead leverage the recent progress in computer …

Multiple hypothesis tracking revisited

C Kim, F Li, A Ciptadi, JM Rehg - Proceedings of the IEEE …, 2015 - cv-foundation.org
This paper revisits the classical multiple hypotheses tracking (MHT) algorithm in a tracking-
by-detection framework. The success of MHT largely depends on the ability to maintain a …

Learning by tracking: Siamese CNN for robust target association

L Leal-Taixé, C Canton-Ferrer… - Proceedings of the IEEE …, 2016 - cv-foundation.org
This paper introduces a novel approach to the task of data association within the context of
pedestrian tracking, by introducing a two-stage learning scheme to match pairs of …

Multi-object tracking with quadruplet convolutional neural networks

J Son, M Baek, M Cho, B Han - Proceedings of the IEEE …, 2017 - openaccess.thecvf.com
Abstract We propose Quadruplet Convolutional Neural Networks (Quad-CNN) for multi-
object tracking, which learn to associate object detections across frames using quadruplet …

Confidence-based data association and discriminative deep appearance learning for robust online multi-object tracking

SH Bae, KJ Yoon - IEEE transactions on pattern analysis and …, 2017 - ieeexplore.ieee.org
Online multi-object tracking aims at estimating the tracks of multiple objects instantly with
each incoming frame and the information provided up to the moment. It still remains a …

Deep network flow for multi-object tracking

S Schulter, P Vernaza, W Choi… - Proceedings of the …, 2017 - openaccess.thecvf.com
Data association problems are an important component of many computer vision
applications, with multi-object tracking being one of the most prominent examples. A typical …

Machine learning methods for data association in multi-object tracking

P Emami, PM Pardalos, L Elefteriadou… - ACM Computing Surveys …, 2020 - dl.acm.org
Data association is a key step within the multi-object tracking pipeline that is notoriously
challenging due to its combinatorial nature. A popular and general way to formulate data …

End-to-end learning of multi-sensor 3d tracking by detection

D Frossard, R Urtasun - 2018 IEEE international conference on …, 2018 - ieeexplore.ieee.org
In this paper we propose a novel approach to tracking by detection that can exploit both
cameras as well as LIDAR data to produce very accurate 3D trajectories. Towards this goal …