Hierarchical Attentive Recurrent Tracking

Understand

Class-agnostic object tracking is particularly difficult in cluttered environments as target specific discriminative models cannot be learned a priori.

• Inspired by how the human visual cortex employs spatial attention and separate "where" and "what" processing pathways to actively suppress irrelevant visual features, this work develops a hierarchical attentive recurrent model for single object tracking in videos.
• The first layer of attention discards the majority of background by selecting a region containing the object of interest, while the subsequent layers tune in on visual features particular to the tracked object.
• This framework is fully differentiable and can be trained in a purely data driven fashion by gradient methods.