Using incremental subspace and contour template for object tracking

Yin, Jingwei; Fu, Chongyang; Hu, Jiankun

doi:10.1016/j.jnca.2012.06.005

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…In order to evaluate the performance of the algorithm, we choose six currently most representative and classic tracking algorithms to do the comparison. The six classic algorithms are L1 Tracker [8], IVT Tracker [14], PN Tracker [15], VTD Tracker [16], MIL Tracker [17], and Frag Tracker [18]. The average processing speeds of our method for different test videos are listed in Table 2.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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Robust Online Object Tracking Based on Feature Grouping and 2DPCA

Jiang

Zhang

2013

Mathematical Problems in Engineering

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We present an online object tracking algorithm based on feature grouping and two-dimensional principal component analysis (2DPCA). Firstly, we introduce regularization into the 2DPCA reconstruction and develop an iterative algorithm to represent an object by 2DPCA bases. Secondly, the object templates are grouped into a more discriminative image and a less discriminative image by computing the variance of the pixels in multiple frames. Then, the projection matrix is learned according to the more discriminative image and the less discriminative image, and the samples are projected. The object tracking results are obtained using Bayesian maximum a posteriori probability estimation. Finally, we employ a template update strategy which combines incremental subspace learning and the error matrix to reduce tracking drift. Compared with other popular methods, our method reduces the computational complexity and is very robust to abnormal changes. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm achieves more favorable performance than several state-of-the-art methods.

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Section: Experimental Results and Analysismentioning

confidence: 99%

“…Intrinsic and extrinsic changes inevitably cause large appearance variation. Due to the nature of the tracking problem, an effective appearance model is of prime importance for the success of a tracking algorithm [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Robust Online Object Tracking Based on Feature Grouping and 2DPCA

Jiang

Zhang

2013

Mathematical Problems in Engineering

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“…At present, there have been a large number of research methods and theories for 2D tracking, such as morphological tracking [33,34], template-based tracking [35,36], kernelized correlation filters method [37], compressed sensing method [38,39], deep-learningbased tracking [40,41], etc. The targets involved in this article have relatively fixed geometric characteristics.…”

Section: Background Of 2d Target Trackingmentioning

confidence: 99%

Dynamic Target Tracking and Ingressing of a Small UAV Using Monocular Sensor Based on the Geometric Constraints

2021

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In many applications of airborne visual techniques for unmanned aerial vehicles (UAVs), lightweight sensors and efficient visual positioning and tracking algorithms are essential in a GNSS-denied environment. Meanwhile, many tasks require the ability of recognition, localization, avoiding, or flying pass through these dynamic obstacles. In this paper, for a small UAV equipped with a lightweight monocular sensor, a single-frame parallel-features positioning method (SPPM) is proposed and verified for a real-time dynamic target tracking and ingressing problem. The solution is featured with systematic modeling of the geometric characteristics of moving targets, and the introduction of numeric iteration algorithms to estimate the geometric center of moving targets. The geometric constraint relationships of the target feature points are modeled as non-linear equations for scale estimation. Experiments show that the root mean square error percentage of static target tracking is less than 1.03% and the root mean square error of dynamic target tracking is less than 7.92 cm. Comprehensive indoor flight experiments are conducted to show the real-time convergence of the algorithm, the effectiveness of the solution in locating and tracking a moving target, and the excellent robustness to measurement noises.

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“…It finds the projection directions along which the reconstruction error to the original data is minimum and projects the original data into a lower dimensional space spanned by those directions corresponding to the top eigenvalues. Recent studies demonstrate that two-dimensional principal component analysis (2DPCA) could achieve performance comparable to PCA with less computational cost [9,10]. Given a series of image matrices Y = [ 1 2 ⋅ ⋅ ⋅ ], 2DPCA aims to obtain an orthogonal left-projection matrix U, an orthogonal right-projection matrix V, and the projection coefficients A = [ 1 2 ⋅ ⋅ ⋅ ] by solving the following objective function:…”

Section: Visual Object Tracking Model Based Onmentioning

confidence: 99%

Visual Object Tracking Based on 2DPCA and ML

Jiang

Wang

2013

Mathematical Problems in Engineering

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We present a novel visual object tracking algorithm based on two-dimensional principal component analysis (2DPCA) and maximum likelihood estimation (MLE). Firstly, we introduce regularization into the 2DPCA reconstruction and develop an iterative algorithm to represent an object by 2DPCA bases. Secondly, the model of sparsity constrained MLE is established. Abnormal pixels in the samples will be assigned with low weights to reduce their effects on the tracking algorithm. The object tracking results are obtained by using Bayesian maximum a posteriori (MAP) probability estimation. Finally, to further reduce tracking drift, we employ a template update strategy which combines incremental subspace learning and the error matrix. This strategy adapts the template to the appearance change of the target and reduces the influence of the occluded target template as well. Compared with other popular methods, our method reduces the computational complexity and is very robust to abnormal changes. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm achieves more favorable performance than several state-of-the-art methods.

show abstract

Using incremental subspace and contour template for object tracking

Cited by 7 publications

References 18 publications

Robust Online Object Tracking Based on Feature Grouping and 2DPCA

Robust Online Object Tracking Based on Feature Grouping and 2DPCA

Dynamic Target Tracking and Ingressing of a Small UAV Using Monocular Sensor Based on the Geometric Constraints

Visual Object Tracking Based on 2DPCA and ML

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