Low-Rank Multi-Channel Features for Robust Visual Object Tracking

Fawad Fawad,Yasar Amin,Muhibur Rahman,Hannu Tenhunen,Muhammad Jamil Khan

doi:10.3390/sym11091155

Abstract

Kernel correlation filters (KCF) demonstrate significant potential in visual object tracking by employing robust descriptors. Proper selection of color and texture features can provide robustness against appearance variations. However, the use of multiple descriptors would lead to a considerable feature dimension. In this paper, we propose a novel low-rank descriptor, that provides better precision and success rate in comparison to state-of-the-art trackers. We accomplished this by concatenating the magnitude component of the Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP), Robustness-Driven Hybrid Descriptor (RDHD), Histogram of Oriented Gradients (HoG), and Color Naming (CN) features. We reduced the rank of our proposed multi-channel feature to diminish the computational complexity. We formulated the Support Vector Machine (SVM) model by utilizing the circulant matrix of our proposed feature vector in the kernel correlation filter. The use of discrete Fourier transform in the iterative learning of SVM reduced the computational complexity of our proposed visual tracking algorithm. Extensive experimental results on Visual Tracker Benchmark dataset show better accuracy in comparison to other state-of-the-art trackers.

Highlights

Visual tracking is the process of Spatio-temporal localization of a moving object in the camera scene
We found that the threshold parameter 0.4 in Equation (10), provides the highest joint maxima for both Area Under the Curve (AUC) and precision when validated on OTB-50 and OTB-2013 dataset
We propose robust low-rank descriptor for kernel support correlation filter

Summary

Introduction

Visual tracking is the process of Spatio-temporal localization of a moving object in the camera scene. Object localization has potential applications, including human activity recognition [1], vehicle navigation [2], surveillance and security [3], and human-machine interaction [4]. The researchers are developing robust trackers to reduce the computational cost of the visual object tracking algorithm. Several reviews on robust tracking techniques have been published [5,6,7]. Visual tracking is desired to be robust against intrinsic variations (e.g., pose, shape deformation, and scale) and extrinsic variations (e.g., background clutter, occlusion, and illumination) [8,9]. The deep learning approaches have achieved a higher accuracy; a massive training data is required, which is not often available in many surveillance applications

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Results

Conclusion