Supervised trackers trained on labeled data dominate the single object
tracking field for superior tracking accuracy. The labeling cost and the huge
computational complexity hinder their applications on edge devices.
Unsupervised learning methods have also been investigated to reduce the
labeling cost but their complexity remains high. Aiming at lightweight
high-performance tracking, feasibility without offline pre-training, and
algorithmic transparency, we propose a new single object tracking method,
called the green object tracker (GOT), in this work. GOT conducts an ensemble
of three prediction branches for robust box tracking: 1) a global object-based
correlator to predict the object location roughly, 2) a local patch-based
correlator to build temporal correlations of small spatial units, and 3) a
superpixel-based segmentator to exploit the spatial information of the target
frame. GOT offers competitive tracking accuracy with state-of-the-art
unsupervised trackers, which demand heavy offline pre-training, at a lower
computation cost. GOT has a tiny model size (<3k parameters) and low inference
complexity (around 58M FLOPs per frame). Since its inference complexity is
between 0.1%-10% of DL trackers, it can be easily deployed on mobile and edge
devices.

提出了一种新的单目标追踪方法 —— 绿色物体追踪器 (GOT)，它能在不进行离线预训练的情况下实现轻量级高性能追踪，并具有算法透明度。与需要离线预训练的最先进的无监督追踪器相比，GOT 具有较低的计算成本和较小的模型尺寸，在保持竞争追踪准确性的同时，适用于移动设备和边缘设备。