Abstract
The challenges of generic visual tracking have attracted great attentions. However, it is still difficult for most of the existing trackers to track objects accurately on real-time occasion. We propose a framework which integrate a verifying mechanism and a correcting mechanism to improve the accuracy of real-time tracking. Under online learning, both target location and sample model update in parallel. Validations are carried out in every frame according to spatial-temporal convolution response. Furthermore, a correcting mechanism would be activated when the current tracking results considered to be unreliable. Synchronously, an online target model updating strategy is constructed to filter the contributive samples, which makes the sample model update confidently. The proposed tracker is evaluated on four popular benchmarks, achieving a state-of-the-art performance while runs at real-time speed.