Multi Target Tracking by Linking Tracklets with a Convolutional Neural Network

Yosra Dorai, Frederic Chausse, Sami Gazzah, Najoua Essoukri Ben Amara

2017

Abstract

The computer vision community has developed many multi-object tracking methods in various fields. The focus is put on traffic scenes and video-surveillance applications where tracking object features are challenging. Indeed, in these particular applications, objects can be partially or totally occluded and can appear differently. Usual detection methods generally fail to leverage those limitations. To deal with this, a framework for multi-object tracking based on the linking of tracklets (mini-trajectories) is proposed. Despite the number of errors (false positives or missing detections) made by the Faster R-CNN detector, short-term Faster R-CNN detection similarities are tracked. The goal is to get tracklets in a given number of frames. We suggest to associate tracklets and apply an update function to correct the trajectories. The experiments show that on the one hand, our approach outperforms the detector to find the undetected objects. And on the other hand, the developed method eliminates the false positives and shows the effectiveness of tracking.

Paper Citation

in Harvard Style

Dorai Y., Chausse F., Gazzah S. and Essoukri Ben Amara N. (2017). Multi Target Tracking by Linking Tracklets with a Convolutional Neural Network . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 492-498. DOI: 10.5220/0006155204920498

in Bibtex Style

@conference{visapp17,
author={Yosra Dorai and Frederic Chausse and Sami Gazzah and Najoua Essoukri Ben Amara},
title={Multi Target Tracking by Linking Tracklets with a Convolutional Neural Network},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={492-498},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006155204920498},
isbn={978-989-758-227-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Multi Target Tracking by Linking Tracklets with a Convolutional Neural Network
SN - 978-989-758-227-1
AU - Dorai Y.
AU - Chausse F.
AU - Gazzah S.
AU - Essoukri Ben Amara N.
PY - 2017
SP - 492
EP - 498
DO - 10.5220/0006155204920498