Object Tracking and Recognition Based on Reliability Assessment of Learning in Mobile Environments

Kim, Youngseop; Han, Woori; Lee, Yong-Hwan; Kim, Cheong Ghil; Kim, Kuinam J.

doi:10.1007/s11277-016-3292-y

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…The continuous convolution operators (C-COT) algorithm [16] uses the method of weighting different filter coefficients for regularization constraints, and the background region is allocated with low coefficients. Kim et al design a tracking algorithm with channel and spatial reliability [17], which divides the candidate region into foreground and background and then performs spatial regularization processing. Ma et al propose a boundaryconstrained tracking algorithm [18], which only activates the coefficients of the object region and forces the coefficients of the corresponding background region in the filter to 0.…”

Section: Tracking By Multi-feature Objective Function Optimizationmentioning

confidence: 99%

Object Tracking Algorithm Based on Multi-Time-Space Perception and Instance-Specific Proposals

Sun¹,

Li²,

Cheng³

2023

Intelligent Automation &Amp; Soft Computing

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Aiming at the problem that a single correlation filter model is sensitive to complex scenes such as background interference and occlusion, a tracking algorithm based on multi-time-space perception and instancespecific proposals is proposed to optimize the mathematical model of the correlation filter (CF). Firstly, according to the consistency of the changes between the object frames and the filter frames, the mask matrix is introduced into the objective function of the filter, so as to extract the spatio-temporal information of the object with background awareness. Secondly, the object function of multi-feature fusion is constructed for the object location, which is optimized by the Lagrange method and solved by closed iteration. In the process of filter optimization, the constraints term of time-space perception is designed to enhance the learning ability of the CF to optimize the final tracking results. Finally, when the tracking results fluctuate, the boundary suppression factor is introduced into the instance-specific proposals to reduce the risk of model drift effectively. The accuracy and success rate of the proposed algorithm are verified by simulation analysis on two popular benchmarks, the object tracking benchmark 2015 (OTB2015) and the temple color 128 (TC-128). Extensive experimental results illustrate that the optimized appearance model of the proposed algorithm is effective. The distance precision rate and overlap success rate of the proposed algorithm are 0.756 and 0.656 on the OTB2015 benchmark, which are better than the results of other competing algorithms. The results of this study can solve the problem of real-time object tracking in the real traffic environment and provide a specific reference for the detection of traffic abnormalities.

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Section: Tracking By Multi-feature Objective Function Optimizationmentioning

confidence: 99%

Object Tracking Algorithm Based on Multi-Time-Space Perception and Instance-Specific Proposals

Sun¹,

Li²,

Cheng³

2023

Intelligent Automation &Amp; Soft Computing

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show abstract

“…The technique possesses the merits of high computational efficiency, high theoretical accuracy, and insensitivity to geometric deformations and differences [ 7 ]. As a result, feature-based image matching has received a lot of attention in the field of computer vision [ 8 , 9 , 10 , 11 ], photogrammetry and remote sensing [ 12 , 13 , 14 , 15 ], in applications [ 16 , 17 , 18 ] such as multiple view 3D reconstruction, remote sensing image fusion and visual localization.…”

Section: Introductionmentioning

confidence: 99%

A Spatial-Frequency Domain Associated Image-Optimization Method for Illumination-Robust Image Matching

Liu

Jia

et al. 2020

Sensors

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Image matching forms an essential means of data association for computer vision, photogrammetry and remote sensing. The quality of image matching is heavily dependent on image details and naturalness. However, complex illuminations, denoting extreme and changing illuminations, are inevitable in real scenarios, and seriously deteriorate image matching performance due to their significant influence on the image naturalness and details. In this paper, a spatial-frequency domain associated image-optimization method, comprising two main models, is specially designed for improving image matching with complex illuminations. First, an adaptive luminance equalization is implemented in the spatial domain to reduce radiometric variations, instead of removing all illumination components. Second, a frequency domain analysis-based feature-enhancement model is proposed to enhance image features while preserving image naturalness and restraining over-enhancement. The proposed method associates the advantages of the spatial and frequency domain analyses to complete illumination equalization, feature enhancement and naturalness preservation, and thus acquiring the optimized images that are robust to the complex illuminations. More importantly, our method is generic and can be embedded in most image-matching schemes to improve image matching. The proposed method was evaluated on two different datasets and compared with four other state-of-the-art methods. The experimental results indicate that the proposed method outperforms other methods under complex illuminations, in both matching performances and practical applications such as structure from motion and multi-view stereo.

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Research of Panoramic Image Generation Using IoT Device with Camera for Cloud Computing Environment

Ahn

Lee

Cho

2018

Wireless Pers Commun

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Object Tracking and Recognition Based on Reliability Assessment of Learning in Mobile Environments

Cited by 3 publications

References 15 publications

Object Tracking Algorithm Based on Multi-Time-Space Perception and Instance-Specific Proposals

Object Tracking Algorithm Based on Multi-Time-Space Perception and Instance-Specific Proposals

A Spatial-Frequency Domain Associated Image-Optimization Method for Illumination-Robust Image Matching

Research of Panoramic Image Generation Using IoT Device with Camera for Cloud Computing Environment

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