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Authors: Juan Diego Gonzales Zuniga ;  Ujjwal and François Bremond

Affiliation: INRIA Sophia Antipolis, 2004 Route des Lucioles, BP93 Sophia Antipolis Cedex, 06902, France

Keyword(s): Multiple Object Tracking, Joint Tracking and Detection, Graph Neural Networks.

Abstract: We propose a unified network for simultaneous detection and tracking. Instead of basing the tracking framework on object detections, we focus our work directly on tracklet detection whilst obtaining object detection. We take advantage of the spatio-temporal information and features from 3D CNN networks and output a series of bounding boxes and their corresponding identifiers with the use of Graph Convolution Neural Networks. We put forward our approach in contrast to traditional tracking-by-detection methods, the major advantages of our formulation are the creation of more reliable tracklets, the enforcement of the temporal consistency, and the absence of data association mechanism for a given set of frames. We introduce DeTracker, a truly joint detection and tracking network. We enforce an intra-batch temporal consistency of features by enforcing a triplet loss over our tracklets, guiding the features of tracklets with different identities separately clustered in the feature space. Our approach is demonstrated on two different datasets, including natural images and synthetic images, and we obtain 58.7% on MOT and 56.79% on a subset of the JTA-dataset. (More)

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Paper citation in several formats:
Zuniga, J.; Ujjwal. and Bremond, F. (2022). DeTracker: A Joint Detection and Tracking Framework. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 526-536. DOI: 10.5220/0010875700003124

@conference{visapp22,
author={Juan Diego Gonzales Zuniga. and Ujjwal. and Fran\c{C}ois Bremond.},
title={DeTracker: A Joint Detection and Tracking Framework},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={526-536},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010875700003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - DeTracker: A Joint Detection and Tracking Framework
SN - 978-989-758-555-5
IS - 2184-4321
AU - Zuniga, J.
AU - Ujjwal.
AU - Bremond, F.
PY - 2022
SP - 526
EP - 536
DO - 10.5220/0010875700003124
PB - SciTePress