  Robust object tracking requires knowledge of tracked objects' appearance,
motion and their evolution over time. Although motion provides distinctive and
complementary information especially for fast moving objects, most of the
recent tracking architectures primarily focus on the objects' appearance
information. In this paper, we propose a two-stream deep neural network tracker
that uses both spatial and temporal features. Our architecture is developed
over ATOM tracker and contains two backbones: (i) 2D-CNN network to capture
appearance features and (ii) 3D-CNN network to capture motion features. The
features returned by the two networks are then fused with attention based
Feature Aggregation Module (FAM). Since the whole architecture is unified, it
can be trained end-to-end. The experimental results show that the proposed
tracker TRAT (TRacking by ATtention) achieves state-of-the-art performance on
most of the benchmarks and it significantly outperforms the baseline ATOM
tracker.
