Online Recommendation-based Convolutional Features for Scale-Aware Visual Tracking
Ran Duan,Changhong Fu,Kostas Alexis,Erdal Kayacan,Ran Duan,Changhong Fu,Kostas Alexis,Erdal Kayacan
In this paper, we develop an online learning-based visual tracking framework that can optimize the target model and estimate the scale variation for object tracking. We propose a recommender-based tracker, which is capable of selecting the representative convolutional neural network (CNN) layers and feature maps autonomously. In addition, the proposed recommender computes the weights of these laye...
