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.
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