Robust Object Tracking via Reverse Low-Rank Sparse Learning and Fractional-Order Variation Regularization

Dan Tian,Guoshan Zhang,Shouyu Zang

doi:10.1155/2020/8640724

Abstract

Object tracking based on low-rank sparse learning usually makes the drift phenomenon occur when the target faces severe occlusion and fast motion. In this paper, we propose a novel tracking algorithm via reverse low-rank sparse learning and fractional-order variation regularization. Firstly, we utilize convex low-rank constraint to force the appearance similarity of the candidate particles, so as to prune the irrelevant particles. Secondly, fractional-order variation is introduced to constrain the sparse coefficient difference in the bounded variation space, which allows the difference between consecutive frames to exist, so as to adapt object fast motion. Meanwhile, fractional-order regularization can restrain severe occlusion by considering more adjacent frames information. Thirdly, we employ an inverse sparse representation method to model the relationship between target candidates and target template, which can reduce the computation complexity for online tracking. Finally, an online updating scheme based on alternating iteration is proposed for tracking computation. Experiments on benchmark sequences show that our algorithm outperforms several state-of-the-art methods, especially exhibiting better adaptability for fast motion and severe occlusion.

Highlights

Visual object tracking is an important technique in computer vision with many applications, such as robotics, medical image analysis, human-computer interaction, and traffic control. e goal of tracking is to predict the motion state of the moving object in the video stream based on the initial state
Dash and Patra [16] propose an effective tracking framework by using a regularized robust sparse coding for representing the Mathematical Problems in Engineering multifeature templates of the candidate objects. ese methods can successfully deal with the target appearance change problem caused by lighting variations and partial occlusions
E main contributions of this work are four-fold: (1) the low-rank constraint is exploited to prune the irrelevant particles; (2) fractional-order variation regularization is introduced to learn the jump information generated by fast motion and complex occlusion; (3) an inverse sparse representation formulation is built to reduce the computation complexity for real-time tracking; and (4) an alternating iteration strategy is presented for online tracking optimization

Summary

Introduction

Visual object tracking is an important technique in computer vision with many applications, such as robotics, medical image analysis, human-computer interaction, and traffic control. e goal of tracking is to predict the motion state of the moving object in the video stream based on the initial state. Ese methods can successfully deal with the target appearance change problem caused by lighting variations and partial occlusions These formulations are not effective for handling fast motion challenges. To solve this problem, we introduce reverse low-rank sparse learning with fractional-order variation regularization for visual object tracking. E main contributions of this work are four-fold: (1) the low-rank constraint is exploited to prune the irrelevant particles; (2) fractional-order variation regularization is introduced to learn the jump information generated by fast motion and complex occlusion; (3) an inverse sparse representation formulation is built to reduce the computation complexity for real-time tracking; and (4) an alternating iteration strategy is presented for online tracking optimization

Problem Formulation

Reverse Low-Rank Sparse Representation Model with

Numerical Implementation

Experimental Results

Quantitative Results

Conclusion