147 research outputs found
GUSOT: Green and Unsupervised Single Object Tracking for Long Video Sequences
Supervised and unsupervised deep trackers that rely on deep learning
technologies are popular in recent years. Yet, they demand high computational
complexity and a high memory cost. A green unsupervised single-object tracker,
called GUSOT, that aims at object tracking for long videos under a
resource-constrained environment is proposed in this work. Built upon a
baseline tracker, UHP-SOT++, which works well for short-term tracking, GUSOT
contains two additional new modules: 1) lost object recovery, and 2)
color-saliency-based shape proposal. They help resolve the tracking loss
problem and offer a more flexible object proposal, respectively. Thus, they
enable GUSOT to achieve higher tracking accuracy in the long run. We conduct
experiments on the large-scale dataset LaSOT with long video sequences, and
show that GUSOT offers a lightweight high-performance tracking solution that
finds applications in mobile and edge computing platforms
- …