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Poster
Hierarchical Attentive Recurrent Tracking
Adam Kosiorek · Alex Bewley · Ingmar Posner

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #120
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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'' andwhat'' 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. To improve training convergence, we augment the loss function with terms for auxiliary tasks relevant for tracking. Evaluation of the proposed model is performed on two datasets: pedestrian tracking on the KTH activity recognition dataset and the more difficult KITTI object tracking dataset.

Keywords: Recurrent Networks · Supervised Deep Networks · Attention Models · Computer Vision · Perception · Video, Motion and Tracking · Biologically Plausible Deep Networks

Author Information

Adam Kosiorek (University of Oxford)
Alex Bewley (Wayve)
Ingmar Posner (Oxford University)

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