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 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. 
Duc Phu Chau (INRIA Sophia Antipolis), Fran\c{c}ois Bremond (INRIA   Sophia Antipolis), Monique Thonnat (INRIA Sophia Antipo...
cs.CV