PENINGKATAN PERFORMANSI KERNEL-BASED OBJECT TRACKING MENGGUNAKAN TYPE-2 FUZZY LOGIC

Object tracking merupakan salah satu bidang pada computer vision yang mempelajari tentang cara melacak suatu objek yang bergerak pada suatu ruang, yang dimana sekarang sedang berkembang dengan pesat. Object tracking dalam pengaplikasiannya digunakan dalam melacak gerakan benda maupun manusia dan augmented reality. Namun dalam pengaplikasiannya object tracking masih memiliki banyak tantangan dalam mendeteksi suatu objek. Pada kernel-based object tracking, kemiripan warna (background clutters) dan pergerakan cepat dari objek (motion blur) merupakan faktor utama penyebab terjadinya kegagalan pelacakan dan juga pada kernel-based object tracking belum dapat mendeteksi kegagalan dalam pelacakan. Sehingga pada algoritma koreksi kegagalan diterapkan pada kernel-based menggunakan type-2 fuzzy logic. Kernel-based object tracking merupakan suatu metode pelacakan objek yang memprediksi lokasi objek berdasarkan histogram warna yang memiliki probabilitas tertinggi. Dalam pengaplikasiannya metode kernel-based kurang akurat dalam mendeteksi objek dikarenakan kemiripan warna background dengan objek yang ditracking serta tracker tetap akan melakukan tracking dengan prediksi yang salah sehingga digunakan type-2 fuzzy logic untuk menganalisis terjadinya kegagalan sesuai dengan rules yang telah ditentukan sebelumnya dan melakukan koreksi pada saat object tracking. Dimana output dari metode kernel-based menjadi input dari type-2 fuzzy logic. Hasil dari algoritma sistem yang dibuat ini dapat meningkatkan kinerja dari metode kernel-based dari algoritma diuji menggunakan Object Tracking Benchmark 50 (OTB-50) berdasarkan parameter Precision Plot dan Success Plot. Hasil percobaan yang telah dilakukan, algoritma yang diusulkan menghasilkan performa sebesar 0.001 lebih presisi dibandingkan dengan kernel-based object tracking yang menghasilkan skor sebesar 0.386 sedangkan proposed system menghasilkan skor sebesar 0.387 berdasarkan parameter precision plot dan memiliki tingkat kesuksesan lebih besar 0.003 dibandingkan dengan kernel-based object tracking yang menghasilkan skor sebesar 0.260 sedangkan proposed system menghasilkan skor sebesar 0.263 diukur berdasarkan parameter success plot

Generalized Kernel-based Visual Tracking

In this work we generalize the plain MS trackers and attempt to overcome standard mean shift trackers' two limitations. It is well known that modeling and maintaining a representation of a target object is an important component of a successful visual tracker. However, little work has been done on building a robust template model for kernel-based MS tracking. In contrast to building a template from a single frame, we train a robust object representation model from a large amount of data. Tracking is viewed as a binary classification problem, and a discriminative classification rule is learned to distinguish between the object and background. We adopt a support vector machine (SVM) for training. The tracker is then implemented by maximizing the classification score. An iterative optimization scheme very similar to MS is derived for this purpose.Comment: 12 page

Object Search Strategy in Tracking Algorithms

The demand for real-time video surveillance systems is increasing rapidly. The purpose of these systems includes surveillance as well as monitoring and controlling the events. Today there are several real-time computer vision applications based on image understanding which emulate the human vision and intelligence. These machines include object tracking as their primary task. Object tracking refers to estimating the trajectory of an object of interest in a video. A tracking system works on the principle of video processing algorithms. Video processing includes a huge amount of data to be processed and this fact dictates while implementing the algorithms on any hardware. However, the problems becomes challenging due to unexpected motion of the object, scene appearance change, object appearance change, structures of objects that are not rigid. Besides this full and partial occlusions and motion of the camera also pose challenges. Current tracking algorithms treat this problem as a classification task and use online learning algorithms to update the object model. Here, we explore the data redundancy in the sampling techniques and develop a highly structured kernel. This kernel acquires a circulant structure which is extremely easy to manipulate. Also, we take it further by using mean shift density algorithm and optical flow by Lucas Kanade method which gives us a heavy improvement in the results

Tracking of Military Objects in a Videosignal

Práce shrnuje a popisuje nejrozšířenější metody pro detekci cílů ve videosignálu. Speciálně se zaměřuje na využítí kernel-based metody s použitím mean-shift algoritmu a metody pro porovnávání vzorů. Obě tyto metody jsou doplněny Kalmanovým filtrem. Výsledek je pak aplikován ke sledování cílů jak v normálním videozáznamu, tak i v záznamu z termokamery. Postup je implementován v C++ s použitím knihnovny OpenCV.First part of this thesis briefly describes the main problems of object tracking in video and methods to deal with them. Main attention is paid towards usage of these methods for target tracking in videos taken by both normal and thermal camera. It propose solution to this problem based on the kernel-based method which uses mean-shift algorhitm and on the template matching. Both tracking methods results are supervised and corrected by Kalman filter. Proposed solution is implemented using C++ with OpenCV library.

Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System

This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour

Robust visual tracking via speedup multiple kernel ridge regression

Most of the tracking methods attempt to build up feature spaces to represent the appearance of a target. However, limited by the complex structure of the distribution of features, the feature spaces constructed in a linear manner cannot characterize the nonlinear structure well. We propose an appearance model based on kernel ridge regression for visual tracking. Dense sampling is fulfilled around the target image patches to collect the training samples. In order to obtain a kernel space in favor of describing the target appearance, multiple kernel learning is introduced into the selection of kernels. Under the framework, instead of a single kernel, a linear combination of kernels is learned from the training samples to create a kernel space. Resorting to the circulant property of a kernel matrix, a fast interpolate iterative algorithm is developed to seek coefficients that are assigned to these kernels so as to give an optimal combination. After the regression function is learned, all candidate image patches gathered are taken as the input of the function, and the candidate with the maximal response is regarded as the object image patch. Extensive experimental results demonstrate that the proposed method outperforms other state-of-the-art tracking methods