Automatic Tracker Selection w.r.t Object Detection Performance
Computer Vision and Pattern Recognition
2014-04-09 v1
Abstract
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.
Cite
@article{arxiv.1404.2005,
title = {Automatic Tracker Selection w.r.t Object Detection Performance},
author = {Duc Phu Chau and François Bremond and Monique Thonnat and Slawomir Bak},
journal= {arXiv preprint arXiv:1404.2005},
year = {2014}
}
Comments
IEEE Winter Conference on Applications of Computer Vision (WACV 2014) (2014)