  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.
