Unsupervised Green Object Tracker (GOT) without Offline Pre-training
Zhiruo Zhou Suya You C. -C. Jay Kuo
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.