GUSOT: Green and Unsupervised Single Object Tracking for Long Video Sequences

• URL: http://arxiv.org/abs/2207.07629v1
• Date: Fri, 15 Jul 2022 17:42:49 GMT
• Title: GUSOT: Green and Unsupervised Single Object Tracking for Long Video Sequences
• Authors: Zhiruo Zhou, Hongyu Fu, Suya You, C.-C. Jay Kuo
• Abstract summary: A green unsupervised single-object tracker, called GUSOT, aims at object tracking for long videos under a resource-constrained environment. GUSOT contains two additional new modules: 1) lost object recovery, and 2) color-saliency-based shape proposal. We conduct experiments on the large-scale dataset LaSOT with long video sequences, and show that GUSOT offers a lightweight high-performance tracking solution.
• Score: 31.600045405499472
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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