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Authors: Yosra Dorai 1 ; Frederic Chausse 2 ; Sami Gazzah 3 and Najoua Essoukri Ben Amara 3

Affiliations: 1 Blaise Pascal University and Sousse University, France ; 2 Blaise Pascal University, France ; 3 Sousse University, Tunisia

Keyword(s): Multi-object Tracking, Tracklet, Faster R-CNN, Traffic Surveillance, Occlusion.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Motion, Tracking and Stereo Vision ; Tracking and Visual Navigation

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 e liminates the false positives and shows the effectiveness of tracking. (More)

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Paper citation in several formats:
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 (VISIGRAPP 2017) - Volume 6: VISAPP; ISBN 978-989-758-227-1; ISSN 2184-4321, SciTePress, pages 492-498. DOI: 10.5220/0006155204920498

@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 (VISIGRAPP 2017) - Volume 6: VISAPP},
year={2017},
pages={492-498},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006155204920498},
isbn={978-989-758-227-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 6: VISAPP
TI - Multi Target Tracking by Linking Tracklets with a Convolutional Neural Network
SN - 978-989-758-227-1
IS - 2184-4321
AU - Dorai, Y.
AU - Chausse, F.
AU - Gazzah, S.
AU - Essoukri Ben Amara, N.
PY - 2017
SP - 492
EP - 498
DO - 10.5220/0006155204920498
PB - SciTePress