Learning transform aware attentive network for object tracking
Abstract Existing trackers often decompose the task of visual tracking into multiple independent components, such as target appearance sampling, classifier learning, and target state inferring. In this paper, we present a transform aware attentive tracking framework, which uses a deep attentive network to directly predict the target states via spatial transform parameters. During off line training, the proposed network learns generic motion patterns of target objects from auxiliary large scale videos. These leaned motion patterns are then applied to track target objects on test sequences. Built on the Spatial Transform Network (STN) the proposed attentive network is fully differentiable and can be trained in an end to end manner. Notably, we only fine tune the pre trained network in the initial frame. The proposed tracker requires neither online model update nor appearance sampling during the tracking process. Extensive experiments on OTB 2013, OTB 2015, VOT 2014 and UAV 123 datasets demonstrate the competitive performance of our method against state of the art attentive tracking methods.
Neurocomputing