Single Object Tracking With Fuzzy Least Squares Support Vector Machine

Abstract

Single object tracking, in which a target is often initialized manually in the first frame and then is tracked and located automatically in the subsequent frames, is a hot topic in computer vision. The traditional tracking-by-detection framework, which often formulates tracking as a binary classification problem, has been widely applied and achieved great success in single object tracking. However, there are some potential issues in this formulation. For instance, the boundary between the positive and negative training samples is fuzzy, and the objectives of tracking and classification are inconsistent. In this paper, we attempt to address the above issues from the fuzzy system perspective and propose a novel tracking method by formulating tracking as a fuzzy classification problem. First, we introduce the fuzzy strategy into tracking and propose a novel fuzzy tracking framework, which can measure the importance of the training samples by assigning different memberships to them and offer more strict spatial constraints. Second, we develop a fuzzy least squares support vector machine (FLS-SVM) approach and employ it to implement a concrete tracker. In particular, the primal form, dual form, and kernel form of FLS-SVM are analyzed and the corresponding closed-form solutions are derived for efficient realizations. Besides, a least squares regression model is built to control the update adaptively, retaining the robustness of the appearance model. The experimental results demonstrate that our method can achieve comparable or superior performance to many state-of-the-art methods.

A fuzzy based Lagrangian twin parametric-margin support vector machine (FLTPMSVM)

“…through the following problems One can write (17) and (18) in the form of dual QPPs by considering the Lagrangian multipliers and apply the KKT min 1 ||K(X , D t )w e b|| 2 C S t ξ necessary and sufficient conditions, which are (12) and (13) are written as the QPPs (19) & (20), one can find the solution of (w ,b ) and…”

“…To solve bankruptcy prediction problem, a new fuzzy SVM is proposed [19]. Further, a fuzzy least squares support vector machine for object tracking is proposed by Zhang et al [20].…”

An Improved Kernelized Correlation Filter with the Histogram in Hue-Saturation-Value Color Space for Object Tracking

“…Li et al [5] introduce a tracker based on online multiple instance boosting, which employs Gaussian Mixture Model and single Gaussian distribution respectively to model features of instances in positive and negative bags and manifests a good performance to handle drift problem. Zhang et al [6] develop a fuzzy least squares Support Vector Machine approach. The approach formulates tracking as a fuzzy classification problem rather than a binary classification problem.…”

“…The former category [1,2,3] extract features of objects to build a model and then search it in the next frame. The latter category [4,5,6] consider the tracking problem as a classification issue which classify the object and background. In addition, deep learning based methods and correlation filters are applied into object tracking.…”

A LS-SVM based Measurement Points Classification Algorithm for Adjacent Targets in WSNs

“…As SVM avoids the traditional process from generalization to deduction, it largely simplifies the usual classification problem. Especially, LS-SVM optimizes the algorithm solution and improves computing efficiency [18,19]. Nevertheless, being a supervised machine learning algorithm, LS-SVM trains the sample data before classification and has been rarely used in applications with dynamic systems, such as tracking problems in WSNs.…”