Real-time tracking-by-learning with high-order regularization fusion for big video abstraction
By considering the object motion is an essential factor that not only needs to be taken care of while sampling during learning, we incorporate the motion information into an online tracking framework with an effective optimization process of the collaborative learning. we model the object motion as a high order regularization, which is composed of a graph-based bidirectional trace regularizer and a kernelized confidence prediction regularizer. For implementing with the abruption motion estimation and budget maintenance, the proposed tracking has demonstrated its advantages in both accuracy and efficiency, which also showed its practicability for big video abstraction applications.
tracking learning high-order regularization video abstraction