Skip to Main content Skip to Navigation
Conference papers

Automatic Tracker Selection w.r.t Object Detection Performance

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [17 references]  Display  Hide  Download
Contributor : Duc Phu Chau <>
Submitted on : Monday, April 7, 2014 - 12:39:49 PM
Last modification on : Thursday, March 5, 2020 - 5:34:16 PM
Long-term archiving on: : Monday, July 7, 2014 - 11:15:35 AM


Files produced by the author(s)


  • HAL Id : hal-00974693, version 1



Duc Phu Chau, François Bremond, Monique Thonnat, Slawomir Bak. Automatic Tracker Selection w.r.t Object Detection Performance. IEEE Winter Conference on Applications of Computer Vision (WACV 2014), Mar 2014, Steamboat Springs CO, United States. ⟨hal-00974693⟩



Record views


Files downloads