• Title/Summary/Keyword: feature-based tracking

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Object Feature Tracking Algorithm based on Siame-FPN (Siame-FPN기반 객체 특징 추적 알고리즘)

  • Kim, Jong-Chan;Lim, Su-Chang
    • Journal of Korea Multimedia Society
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    • v.25 no.2
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    • pp.247-256
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    • 2022
  • Visual tracking of selected target objects is fundamental challenging problems in computer vision. Object tracking localize the region of target object with bounding box in the video. We propose a Siam-FPN based custom fully CNN to solve visual tracking problems by regressing the target area in an end-to-end manner. A method of preserving the feature information flow using a feature map connection structure was applied. In this way, information is preserved and emphasized across the network. To regress object region and to classify object, the region proposal network was connected with the Siamese network. The performance of the tracking algorithm was evaluated using the OTB-100 dataset. Success Plot and Precision Plot were used as evaluation matrix. As a result of the experiment, 0.621 in Success Plot and 0.838 in Precision Plot were achieved.

Object Tracking using Feature Map from Convolutional Neural Network (컨볼루션 신경망의 특징맵을 사용한 객체 추적)

  • Lim, Suchang;Kim, Do Yeon
    • Journal of Korea Multimedia Society
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    • v.20 no.2
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    • pp.126-133
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    • 2017
  • The conventional hand-crafted features used to track objects have limitations in object representation. Convolutional neural networks, which show good performance results in various areas of computer vision, are emerging as new ways to break through the limitations of feature extraction. CNN extracts the features of the image through layers of multiple layers, and learns the kernel used for feature extraction by itself. In this paper, we use the feature map extracted from the convolution layer of the convolution neural network to create an outline model of the object and use it for tracking. We propose a method to adaptively update the outline model to cope with various environment change factors affecting the tracking performance. The proposed algorithm evaluated the validity test based on the 11 environmental change attributes of the CVPR2013 tracking benchmark and showed excellent results in six attributes.

Feature tracking algorithm using multi resolution in wavelet transform domain (웨이브릿 변환 영역에서 다중 해상도를 이용한 특징점 추적 알고리즘)

  • Jang, Sung-Kun;Suk, Jung-Youp;Jin, Sang-Hun;Kim, Sung-Un;Yeo, Bo-Yeon
    • Proceedings of the IEEK Conference
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    • 2006.06a
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    • pp.447-448
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    • 2006
  • In this paper, we propose tracking algorithm using multi resolution in wavelet transform domain. This algorithm consists of two steps. The first step is feature extraction that is select feature-points using 1-level wavelet transform in ROI (Region of Interest). The other step is feature tracking. Based on multi resolution of wavelet transform, we estimate a displacement between current frame and next frame on the basis of selected feature-points. Experimental results show that the proposed algorithm confirmed a better performance than a centroid tracking and correlation tracking.

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Particle Filter Based Feature Points Tracking for Vision Based Navigation System (영상기반항법을 위한 파티클 필터 기반의 특징점 추적 필터 설계)

  • Won, Dae-Hee;Sung, Sang-Kyung;Lee, Young-Jae
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.40 no.1
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    • pp.35-42
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    • 2012
  • In this study, a feature-points-tracking algorithm is suggested using a particle filter for vision based navigation system. By applying a dynamic model of the feature point, the tracking performance is increased in high dynamic condition, whereas a conventional KLT (Kanade-Lucas-Tomasi) cannot give a solution. Futhermore, the particle filter is introduced to cope with irregular characteristics of vision data. Post-processing of recorded vision data shows that the tracking performance of suggested algorithm is more robust than that of KLT in high dynamic condition.

3D FACE RECONSTRUCTION FROM ROTATIONAL MOTION

  • Sugaya, Yoshiko;Ando, Shingo;Suzuki, Akira;Koike, Hideki
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2009.01a
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    • pp.714-718
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    • 2009
  • 3D reconstruction of a human face from an image sequence remains an important problem in computer vision. We propose a method, based on a factorization algorithm, that reconstructs a 3D face model from short image sequences exhibiting rotational motion. Factorization algorithms can recover structure and motion simultaneously from one image sequence, but they usually require that all feature points be well tracked. Under rotational motion, however, feature tracking often fails due to occlusion and frame out of features. Additionally, the paucity of images may make feature tracking more difficult or decrease reconstruction accuracy. The proposed 3D reconstruction approach can handle short image sequences exhibiting rotational motion wherein feature points are likely to be missing. We implement the proposal as a reconstruction method; it employs image sequence division and a feature tracking method that uses Active Appearance Models to avoid the failure of feature tracking. Experiments conducted on an image sequence of a human face demonstrate the effectiveness of the proposed method.

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An Approach to Target Tracking Using Region-Based Similarity of the Image Segmented by Least-Eigenvalue (최소고유치로 분할된 영상의 영역기반 유사도를 이용한 목표추적)

  • Oh, Hong-Gyun;Sohn, Yong-Jun;Jang, Dong-Sik;Kim, Mun-Hwa
    • Journal of Institute of Control, Robotics and Systems
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    • v.8 no.4
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    • pp.327-332
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    • 2002
  • The main problems of computational complexity in object tracking are definition of objects, segmentations and identifications in non-structured environments with erratic movements and collisions of objects. The object's information as a region that corresponds to objects without discriminating among objects are considered. This paper describes the algorithm that, automatically and efficiently, recognizes and keeps tracks of interest-regions selected by users in video or camera image sequences. The block-based feature matching method is used for the region tracking. This matching process considers only dominant feature points such as corners and curved-edges without requiring a pre-defined model of objects. Experimental results show that the proposed method provides above 96% precision for correct region matching and real-time process even when the objects undergo scaling and 3-dimen-sional movements In successive image sequences.

Bottleneck-based Siam-CNN Algorithm for Object Tracking (객체 추적을 위한 보틀넥 기반 Siam-CNN 알고리즘)

  • Lim, Su-Chang;Kim, Jong-Chan
    • Journal of Korea Multimedia Society
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    • v.25 no.1
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    • pp.72-81
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    • 2022
  • Visual Object Tracking is known as the most fundamental problem in the field of computer vision. Object tracking localize the region of target object with bounding box in the video. In this paper, a custom CNN is created to extract object feature that has strong and various information. This network was constructed as a Siamese network for use as a feature extractor. The input images are passed convolution block composed of a bottleneck layers, and features are emphasized. The feature map of the target object and the search area, extracted from the Siamese network, was input as a local proposal network. Estimate the object area using the feature map. The performance of the tracking algorithm was evaluated using the OTB2013 dataset. Success Plot and Precision Plot were used as evaluation matrix. As a result of the experiment, 0.611 in Success Plot and 0.831 in Precision Plot were achieved.

Implementation of Improved Object Detection and Tracking based on Camshift and SURF for Augmented Reality Service (증강현실 서비스를 위한 Camshift와 SURF를 개선한 객체 검출 및 추적 구현)

  • Lee, Yong-Hwan;Kim, Heung-Jun
    • Journal of the Semiconductor & Display Technology
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    • v.16 no.4
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    • pp.97-102
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    • 2017
  • Object detection and tracking have become one of the most active research areas in the past few years, and play an important role in computer vision applications over our daily life. Many tracking techniques are proposed, and Camshift is an effective algorithm for real time dynamic object tracking, which uses only color features, so that the algorithm is sensitive to illumination and some other environmental elements. This paper presents and implements an effective moving object detection and tracking to reduce the influence of illumination interference, which improve the performance of tracking under similar color background. The implemented prototype system recognizes object using invariant features, and reduces the dimension of feature descriptor to rectify the problems. The experimental result shows that that the system is superior to the existing methods in processing time, and maintains better problem ratios in various environments.

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Object Tracking with Sparse Representation based on HOG and LBP Features

  • Boragule, Abhijeet;Yeo, JungYeon;Lee, GueeSang
    • International Journal of Contents
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    • v.11 no.3
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    • pp.47-53
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    • 2015
  • Visual object tracking is a fundamental problem in the field of computer vision, as it needs a proper model to account for drastic appearance changes that are caused by shape, textural, and illumination variations. In this paper, we propose a feature-based visual-object-tracking method with a sparse representation. Generally, most appearance-based models use the gray-scale pixel values of the input image, but this might be insufficient for a description of the target object under a variety of conditions. To obtain the proper information regarding the target object, the following combination of features has been exploited as a corresponding representation: First, the features of the target templates are extracted by using the HOG (histogram of gradient) and LBPs (local binary patterns); secondly, a feature-based sparsity is attained by solving the minimization problems, whereby the target object is represented by the selection of the minimum reconstruction error. The strengths of both features are exploited to enhance the overall performance of the tracker; furthermore, the proposed method is integrated with the particle-filter framework and achieves a promising result in terms of challenging tracking videos.

Online Face Avatar Motion Control based on Face Tracking

  • Wei, Li;Lee, Eung-Joo
    • Journal of Korea Multimedia Society
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    • v.12 no.6
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    • pp.804-814
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    • 2009
  • In this paper, a novel system for avatar motion controlling by tracking face is presented. The system is composed of three main parts: firstly, LCS (Local Cluster Searching) method based face feature detection algorithm, secondly, HMM based feature points recognition algorithm, and finally, avatar controlling and animation generation algorithm. In LCS method, face region can be divided into many small piece regions in horizontal and vertical direction. Then the method will judge each cross point that if it is an object point, edge point or the background point. The HMM method will distinguish the mouth, eyes, nose etc. from these feature points. Based on the detected facial feature points, the 3D avatar is controlled by two ways: avatar orientation and animation, the avatar orientation controlling information can be acquired by analyzing facial geometric information; avatar animation can be generated from the face feature points smoothly. And finally for evaluating performance of the developed system, we implement the system on Window XP OS, the results show that the system can have an excellent performance.

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