Robust Visual Tracking by Motion Analyzing

TL;DR

This paper introduces a motion analysis-based algorithm for visual tracking that leverages tensor decomposition to improve accuracy and enable real-time performance across multiple benchmarks.

Contribution

It presents a novel motion pattern analysis method using Tucker2 tensor decomposition for robust, real-time visual tracking, outperforming existing methods on key benchmarks.

Findings

Achieved state-of-the-art results on LaSOT, AVisT, OTB100, and GOT-10k.

Capable of real-time operation in practical scenarios.

Effectively describes target motion using tensor-based analysis.

Abstract

In recent years, Video Object Segmentation (VOS) has emerged as a complementary method to Video Object Tracking (VOT). VOS focuses on classifying all the pixels around the target, allowing for precise shape labeling, while VOT primarily focuses on the approximate region where the target might be. However, traditional segmentation modules usually classify pixels frame by frame, disregarding information between adjacent frames. In this paper, we propose a new algorithm that addresses this limitation by analyzing the motion pattern using the inherent tensor structure. The tensor structure, obtained through Tucker2 tensor decomposition, proves to be effective in describing the target's motion. By incorporating this information, we achieved competitive results on Four benchmarks LaSOT\cite{fan2019lasot}, AVisT\cite{noman2022avist}, OTB100\cite{7001050}, and GOT-10k\cite{huang2019got} LaSOT\cite{fan2019lasot} with SOTA. Furthermore, the proposed tracker is capable of real-time operation, adding value to its practical application.