GUSOT: Green and Unsupervised Single Object Tracking for Long Video Sequences
This work addresses resource-constrained tracking for mobile and edge computing platforms, but it is incremental as it builds upon an existing baseline tracker.
The paper tackled the problem of high computational and memory costs in object tracking for long videos by proposing GUSOT, a green unsupervised tracker, which achieved higher tracking accuracy on the LaSOT dataset.
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.