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.
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https://hal.inria.fr/hal-00974693
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Submitted on : Monday, April 7, 2014 - 12:39:49 PM
Last modification on : Tuesday, July 24, 2018 - 3:48:06 PM
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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⟩

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