  Efficiently modeling spatio-temporal relations of objects is a key challenge
in visual object tracking (VOT). Existing methods track by appearance-based
similarity or long-term relation modeling, resulting in rich temporal contexts
between consecutive frames being easily overlooked. Moreover, training trackers
from scratch or fine-tuning large pre-trained models needs more time and memory
consumption. In this paper, we present ACTrack, a new tracking framework with
additive spatio-temporal conditions. It preserves the quality and capabilities
of the pre-trained Transformer backbone by freezing its parameters, and makes a
trainable lightweight additive net to model spatio-temporal relations in
tracking. We design an additive siamese convolutional network to ensure the
integrity of spatial features and perform temporal sequence modeling to
simplify the tracking pipeline. Experimental results on several benchmarks
prove that ACTrack could balance training efficiency and tracking performance.
