In the long time tracking

object representation and occlusion handling are two important challenges. We propose a selective tracking and detection framework in which a new probabilistic object-enhanced feature is integrated. Firstly

we propose a foreground probability map to enhance the target and weaken the surrounding background. Secondly

we introduce the selective tracking and detection framework that has two sets of conditions to control the detector activation and final result selection. We have evaluated our methods on the popular benchmark OTB2013 dataset. The algorithm achieves an overall accuracy of 91.1% and a success rate of 67%

which demonstrates that our algorithm performs favorably compared with other state-of-the-art methods.