DeTracker: A Joint Detection and Tracking Framework

Juan Diego Gonzales Zuniga, Ujjwal, François Bremond

2022

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.

Paper Citation

in Harvard Style

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, SciTePress, pages 526-536. DOI: 10.5220/0010875700003124

in Bibtex Style

@conference{visapp22,
author={Juan Diego Gonzales Zuniga and Ujjwal and François 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},
}

in EndNote Style

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
AU - Zuniga J.
AU - Ujjwal.
AU - Bremond F.
PY - 2022
SP - 526
EP - 536
DO - 10.5220/0010875700003124
PB - SciTePress