Efficient Visual Tracking with Exemplar Transformers

The design of more complex and powerful neural network models has significantly advanced the state-of-the-art in visual object tracking. These advances can be attributed to deeper networks, or to the introduction of new building blocks, such as transformers. However, in the pursuit of increased tracking performance, efficient tracking architectures have received surprisingly little attention. In this paper, we introduce the Exemplar Transformer, an efficient transformer for real-time visual object tracking. E.T.Track, our visual tracker that incorporates Exemplar Transformer layers, runs at 47 fps on a CPU. This is up to 8× faster than other transformer-based models, making it the only real-time transformer-based tracker. When compared to lightweight trackers that can operate in real-time on standard CPUs, E.T.Track consistently outperforms all other methods on the LaSOT [12], OTB-100 [32], NFS [18], TrackingNet [24] and VOT-ST2020 [19] datasets. The code will soon be released on PyTracking.