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visual object tracking Performance Measures Revisited

IEEE TRANSACTIONS ON IMAGE PROCESSING 2016年第3期25卷 1261-1274页

作者： Cehovin, Luka Leonardis, Ales Kristan, Matej Univ Ljubljana Fac Comp & Informat Sci Ljubljana 1000 Slovenia Univ Birmingham Sch Comp Sci Ctr Computat Neurosci & Cognit Robot Birmingham B15 2TT W Midlands England

The problem of visual tracking evaluation is sporting a large variety of performance measures, and largely suffers from lack of consensus about which measures should be used in experiments. This makes the cross-paper tracker comparison difficult. Furthermore, as some measures may be less effective than others, the tracking results may be skewed or biased toward particular tracking aspects. In this paper, we revisit the popular performance measures and tracker performance visualizations and analyze them theoretically and experimentally. We show that several measures are equivalent from the point of information they provide for tracker comparison and, crucially, that some are more brittle than the others. Based on our analysis, we narrow down the set of potential measures to only two complementary ones, describing accuracy and robustness, thus pushing toward homogenization of the tracker evaluation methodology. These two measures can be intuitively interpreted and visualized and have been employed by the recent visual object tracking challenges as the foundation for the evaluation methodology.

关键词： visual object tracking performance evaluation performance measures experimental evaluation

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visual object tracking with Autoencoder Representations 24

Visual Object Tracking with Autoencoder Representations

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24th Signal Processing and Communication Application Conference (SIU)

作者： Besbinar, Beril Alatan, A. Aydin Orta Dogu Tekn Univ Elekt & Elekt Muhendisligi Ankara Turkey ODTU Goruntu Anal Uygulama & Arastirma Merkezi OG Ankara Turkey

ISBN: (纸本)9781509016792

Deep learning is the discipline of training computational models that are composed of multiple layers and these methods have recently improved the state of the art in many areas as a virtue of large labeled datasets, increase in the computational power of current hardware and unsupervised training methods. Although such a dataset may not be available for lots of application areas, the representations obtained by the well-designed networks that have a large representation capacity and trained with enough data are claimed to have the ability to generalize for transfer learning. As an example application, in this work, we investigate the use of stacked autoencoders for visual object tracking, which is a challenging yet very important task in computer vision. Training of autoencoders is achieved via an auxiliary dataset and the resultant representations are utilized within the tracking-by-detection framework. Experiments, realized using a challenge toolkit, indicate that exploiting the intricate structure in auxiliary dataset via hierarchical representations contributes to the solution of visual object tracking problem.

关键词： visual object tracking autoencoders deep learning particle filter

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visual object tracking Using Discriminative Correlation Filter

Visual Object Tracking Using Discriminative Correlation Filt...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Ramalakshmi, V. Alex, M. Germanus Bharathiar Univ Dept Comp Sci Coimbatore Tamil Nadu India

ISBN: (纸本)9781509010660

Video recording systems are becoming ubiquitous today towards several critical applications including automated video surveillance, traffic monitoring, and smart environments. visual object tracking is one of challenging and emerging research topics in the recent past in the domain of image processing and computer vision. It is the process of locating and tracking the motion and orientation of one or multiple moving objects over time in the given video sequence. In this paper, we present the design and implementation of a visual object tracking system using video scenes captured from a single surveillance camera. The proposed system uses a discriminative correlation filter based model which is robust and computationally efficient for real time tracking. The proposed system is developed as IPython notebook using several open source libraries. Experiments are conducted on three real world publicly available benchmarking video sequences. Our experimental results show that maximum center location error is only 5 pixels while achieving an average success rate of 80% in tracking the object of the interest.

关键词： Video image processing object identification object tracking visual object tracking Correlation filter

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Collaborative visual object tracking via Hierarchical Structure 8th

Collaborative Visual Object Tracking via Hierarchical Struct...

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8th International Conference on Social Robotics (ICSR)

作者： Tu, Fangwen Ge, Shuzhi Sam Suryadi, Henry Pratama Tang, Yazhe Hang, Chang Chieh Natl Univ Singapore Dept Elect & Comp Engn Singapore 117575 Singapore

ISBN: (纸本)9783319474373;9783319474366

This paper focuses on the optimization and improvement of visual-based object tracking algorithm. Reflecting from previously used tracking algorithm, we approach the problem using L2-regularized least squares to solve the sparse representation matrix of the object appearance model and propose an efficient collaborative algorithm to track the object. A hierarchical framework and selective multi-memory based online dictionary update are developed to upgrade the speed of the algorithm and improve the robustness by considering both current and history appearance into the template. In addition, key-point feature matching is novelly proposed to further enhance the accuracy of the tracking algorithm by calculating an optical flow based similarity degree. Finally, the proposed algorithm is verified using comprehensive image sequence datasets to demonstrate its effectiveness on coping with various tracking challenges, such as object deformations, illumination changes and partial occlusions.

关键词： Hierarchical framework Key-point features Multi-memory template update visual object tracking

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visual object tracking using Particle Filtering with Dual Manifold Models

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2015年第1期31卷 283-296页

作者： Xie, Yinghong Wu, Chengdong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Shenyang Univ Sch Informat Engn Shenyang 110044 Peoples R China

Compared with affine transformation, projection transformation represents the process of imaging objects more accurately. This paper proposes a novel object tracking method using particle filtering with dual manifold models. One is the covariance manifold used for the object observation model, and the other is the geometric deformation on SL(3) group, where the rank of projection transformation matrix equals 1, adapted to utilize for object dynamic model. Our main contribution is to utilize both the geometry of SL(3) group and covariance manifolds in developing a general particle filtering-based tracking algorithm. Extensive experiments prove that the proposed method can realize stable and accurate tracking of object with significant geometric deformation, even with illumination changes and when an object is obscured.

关键词： visual object tracking SL(3) group Riemannian manifold covariance dual models

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visual object tracking BASED ON PERCEPTUAL HASH ALGORITHM 12

VISUAL OBJECT TRACKING BASED ON PERCEPTUAL HASH ALGORITHM

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12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Fan, Fengjiao Gao, Guoming Li, Jianping Univ Elect & Technol China Sch Engn & Comp Sci Chengdu 611731 Peoples R China

ISBN: (纸本)9781467382663

visual object tracking (VOT) is one ofthe most challenging problems in the field of computer vision, which plays a crucial role in many applications, especially in human-computer interaction, surveillance and robotics. This paper investigates the contribution of perceptual hash algorithm (PHA) in visual object tracking, which is to generate a "fingerprint" string for each image, and then compare the fingerprint of different images. The results are closer, which means the pictures are more similar. We use the hash fingerprint to conduct the object tracking. Our results suggest that perceptual hash algorithm attributes provide superior performance for visual object tracking, and effectively improve the image match.

关键词： visual object tracking PHA fingerprint

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visual object tracking Using Improved Mean Shift Algorithm 2

Visual Object Tracking Using Improved Mean Shift Algorithm

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International Conference on Information Technology Systems and Innovation (ICITSI)

作者： Setyawan, Sulfan Bagus Purwanto, Djoko Mardiyanto, Ronny Inst Teknol Sepuluh Nopember Dept Elect Engn Surabaya Indonesia

ISBN: (纸本)9781467366649

visual object tracking is one of many important applications for surveillance systems. The issues for visual object tracking are robustness from background interference, scaling and occlusion detection. In this paper, visual object tracking using improved Mean Shift algorithm is proposed. Mean Shift algorithm is used to obtain center object target for tracking. Corrected Background Weighted Histogram is added in target model to reduce background interference. Then, Scale adaptive methods is added in Mean Shift for scaling. Occlusion detection is handled by scaled Normalized Cross Correlation. The results prove that the proposed method is robust from noise background, scaling and occlusion detection.

关键词： visual object tracking Mean Shift Corrected Background Weighted Histogram Scaling Normalized Cross Correlation

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Arbitration algorithm of FIR filter and optical flow based on ANFIS for visual object tracking

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MEASUREMENT 2015年 75卷 338-353页

作者： Choi, In Hwan Pak, Jung Min Ahn, Choon Ki Lee, Seung Han Lim, Myo Taeg Song, Moon Kyou Korea Univ Sch Elect Engn Seoul South Korea Wonkwang Univ Dept Elect Convergence Engn Iksan South Korea

This paper proposes a new visual object tracking algorithm based on an adaptive neuro fuzzy inference system (ANFIS) for arbitration algorithm between a finite impulse response (FIR) filter and optical flow (OF). The proposed algorithm is called ANFIS-based FIR filter and OF arbitration (AFOA). The AFOA operates as an FIR filter for normal situations, keeping the computational cost low, and, when abrupt turns occur, converts to an OF to compensate for the inaccuracy of the FIR filter. An ANFIS-based arbitration algorithm constructs a mapping system from given inputs to an output using fuzzy logic and determines tracking mode of the tracking process between the FIR filter and the OF. The effectiveness of the AFOA algorithm is demonstrated by experiments employed on real-time video clips along with a comparative analysis with the ANFIS-based Kalman filter and OF arbitration (AKOA). (C) 2015 Elsevier Ltd. All rights reserved.

关键词： visual object tracking Finite impulse response (FIR) filter Optical flow Adaptive neuro fuzzy inference system (ANFIS) ANFIS-based FIR filter and OF arbitration (AFOA)

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An Adaptive Approach for Validation in visual object tracking 2

An Adaptive Approach for Validation in Visual Object Trackin...

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2nd International Symposium on Computer Vision and the Internet (VisionNet)

作者： Shinora, Jasper Princy W. Agilandeeswari, L. Muralibabu, K. VIT Univ SITE Vellore 632014 Tamil Nadu India Global Inst Engn & Technol Vellore 632001 Tamil Nadu India

One of the major goals in the field of computer vision is to enable computers to replicate the basic functions of human vision such as motion perception and scene understanding. To achieve the goal of intelligent motion perception, much effort has been spent on visual object tracking. Research interest in visual object tracking comes from the fact that it has a wide range of real-world applications. The uncertainty of validating unpredictable features in object tracking is a challenging task in visual object tracking with occlusion and large appearance variation. To address this uncertainty, we propose an adaptive approach which uses updating model based on the occlusion and distortion parameters. In case of occlusion or large appearance variation, the proposed method uses backward model validation where it updates the invalid appearance and then validates the target feature model. If the target feature did not undergo any kind of clutter or distortions, it simply validates and then updates the appearance model using forward feature validation. The experimental results obtained from this adaptive approach demonstrate effectiveness in terms of OR (Overlap Rate) and Center Location Error, compared with other relevant existing algorithms. (C) 2015 Published by Elsevier B.V.

关键词： visual object tracking Adaptive model object Occlusion Backward Model Validation Forward Model Validation

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visual object tracking BASED ON PERCEPTUAL HASH ALGORITHM

VISUAL OBJECT TRACKING BASED ON PERCEPTUAL HASH ALGORITHM

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International Copmputer Conference on Wavelet Active Miedia Technology and Information Processing

作者： FENGJIAO FAN GUOMING GAO JIANPING LI School of Computer Science and Engineering University of Electronic and Technology of China Chengdu 611731 China

ISBN: (纸本)9781467382670

visual object tracking (VOT) is one of the most challenging problems in the field of computer vision, which plays a crucial role in many applications, especially in human-computer interaction, surveillance and robotics. This paper investigates the contribution of perceptual hash algorithm (PHA) in visual object tracking, which is to generate a "fingerprint" string for each image, and then compare the fingerprint of different images. The results are closer, which means the pictures are more similar. We use the hash fingerprint to conduct the object tracking. Our results suggest that perceptual hash algorithm attributes provide superior performance for visual object tracking, and effectively improve the image match.

关键词： visual object tracking PHA fingerprint

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