Automatic tracker selection w.r.t object detection performance

Automatic tracker selection w.r.t object detection performance

The tracking algorithm performance depends on video content. This paper presents a new multi-object tracking approach which is able to cope with video content variations. First the object detection is improved using Kanade-Lucas-Tomasi (KLT) feature tracking. Second, for each mobile object, an appro...

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Bibliographic Details
Published in:IEEE Winter Conference on Applications of Computer Vision pp. 870 - 876
Main Authors: Duc Phu Chau, Bremond, Francois, Thonnat, Monique, Bak, Slawomir
Format: Conference Proceeding
Language:English
Published: IEEE 01-03-2014
Subjects:
Color
Feature extraction
Histograms
Mobile communication
Object tracking
Shape
Online Access:Get full text
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Summary:The tracking algorithm performance depends on video content. This paper presents a new multi-object tracking approach which is able to cope with video content variations. First the object detection is improved using Kanade-Lucas-Tomasi (KLT) feature tracking. Second, for each mobile object, an appropriate tracker is selected among a KLT-based tracker and a discriminative appearance-based tracker. This selection is supported by an online tracking evaluation. The approach has been experimented on three public video datasets. The experimental results show a better performance of the proposed approach compared to recent state of the art trackers.
ISSN:1550-5790
2642-9381
DOI:10.1109/WACV.2014.6836012