Modification of Tracking Algorithm Struck by the Application of the Scale Estimation Method

2018 ◽  
Author(s):  
Vitalii A. Pavlov ◽  
Sergey V. Zavjalov ◽  
Sergey V. Volvenko ◽  
Marina A. Zanina
Keyword(s):  
Estimation Method ◽  
Tracking Algorithm ◽  
Scale Estimation

An adaptive scale estimation target tracking algorithm based on UAV

2020 ◽  
Author(s):  
Rongyao Zhang ◽  
Shanlin Sun ◽  
Yun Li ◽  
Zhitian Li ◽  
Kai Tian
Keyword(s):  
Target Tracking ◽  
Tracking Algorithm ◽  
Scale Estimation

scholarly journals DOA Tracking of Two-Dimensional Coherent Distribution Source Based on Fast Approximated Power Iteration

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Tao Wu ◽  
Pengtao Zhang ◽  
Yiwen Li ◽  
Yangjun Gao ◽  
Chaoqi Fu ◽  
...  
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Distribution Functions ◽  
Tracking Algorithm ◽  
Signal Subspace ◽  
Two Dimensional ◽  
Model Errors ◽  
Rectangular Array ◽  
Linear Arrays ◽  
Power Iteration

Aiming at two-dimensional (2D) coherent distributed (CD) sources, this paper has proposed a direction of arrival (DOA) tracking algorithm based on signal subspace updating under the uniform rectangular array (URA). First, based on the hypothesis of small angular spreads of distributed sources, the rotating invariant relations of the signal subspace of the receive vector of URA are derived. An ESPRIT-like method is constructed for DOA estimation using two adjacent parallel linear arrays of URA. Through the synthesis of estimation by multiple groups of parallel linear arrays within URA arrays, the DOA estimation method for 2D CD sources based on URA is obtained. Then, fast approximated power iteration (FAPI) subspace tracking algorithm is used to update the signal subspace. In this way, DOA tracking of 2D CD sources can be realized by DOA estimation through signal subspace updating. This algorithm has a low computational complexity and good real-time tracking performance. In addition, the algorithm can track multiple CD sources without knowing the angular signal distribution functions, which is robust to model errors.

scholarly journals Fast Scale Estimation Method in Object Tracking

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 31057-31068
Author(s):  
Xianglei Yin ◽  
Guixi Liu ◽  
Xiaohong Ma
Keyword(s):  
Object Tracking ◽  
Estimation Method ◽  
Scale Estimation

scholarly journals Robust long-term object tracking with adaptive scale and rotation estimation

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142090973
Author(s):  
Huimin Lu ◽  
Dan Xiong ◽  
Junhao Xiao ◽  
Zhiqiang Zheng
Keyword(s):  
Object Tracking ◽  
Estimation Method ◽  
Tracking Algorithm ◽  
Correlation Filter ◽  
Estimation Accuracy ◽  
Continuous Space ◽  
Searching Method ◽  
Object Searching ◽  
Rotation Estimation

In this article, a robust long-term object tracking algorithm is proposed. It can tackle the challenges of scale and rotation changes during the long-term object tracking for security robots. Firstly, a robust scale and rotation estimation method is proposed to deal with scale changes and rotation motion of the object. It is based on the Fourier–Mellin transform and the kernelized correlation filter. The object’s scale and rotation can be estimated in the continuous space, and the kernelized correlation filter is used to improve the estimation accuracy and robustness. Then a weighted object searching method based on the histogram and the variance is introduced to handle the problem that trackers may fail in the long-term object tracking (due to semi-occlusion or full occlusion). When the tracked object is lost, the object can be relocated in the whole image using the searching method, so the tracker can be recovered from failures. Moreover, two other kernelized correlation filters are learned to estimate the object’s translation and the confidence of tracking results, respectively. The estimated confidence is more accurate and robust using the dedicatedly designed kernelized correlation filter, which is employed to activate the weighted object searching module, and helps to determine whether the searching windows contain objects. We compare the proposed algorithm with state-of-the-art tracking algorithms on the online object tracking benchmark. The experimental results validate the effectiveness and superiority of our tracking algorithm.

Outlier Detection by means of Monte Carlo Estimation including resistant Scale Estimation

2015 ◽  
Vol 9 (2) ◽  
Author(s):  
Christian Marx
Keyword(s):  
Monte Carlo ◽  
Measurement Data ◽  
Estimation Method ◽  
Bayes Estimation ◽  
Scale Estimation ◽  
Unknown Variance ◽  
Least Squares Adjustment ◽  
Monte Carlo Search ◽  
Monte Carlo Estimation ◽  
Variance Factor

AbstractThe identification of outliers in measurement data is hindered if they are present in leverage points as well as in rest of the data. A promising method for their identification is the Monte Carlo estimation (MCE), which is subject of the present investigation. In MCE the data are searched for data subsamples without leverage outliers and with few (or no) non-leverage outliers by a random generation of subsamples. The required number of subsamples by which several of such subsamples are generated with a given probability is derived. Each generated subsample is rated based on the residuals resulting from an adjustment. By means of a simulation it is shown that a least squares adjustment is suitable. For the rating of the subsamples, the sum of squared residuals is used as a measure of the fit. It is argued that this (unweighted) sum is also appropriate if data have unequal weights. An investigation of the robustness of a final Bayes estimation with the result of the Monte Carlo search as prior information reveals its inappropriateness. Furthermore, the case of an unknown variance factor is considered. A simulation for different scale estimators for the variance factor shows their impracticalness. A new resistant scale estimator is introduced which is based on a generalisation of the median absolut deviation. Taking into account the results of the investigations, a new procedure for MCE considering a scale estimation is proposed. Finally, this method is tested by simulation. MCE turns out to be more reliable in the identification of outliers than a conventional resistant estimation method.

scholarly journals A compressed multiple feature and adaptive scale estimation method for correlation filter-based visual tracking

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141775151 ◽  
Author(s):  
ZL Wang ◽  
BG Cai
Keyword(s):  
Visual Tracking ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Color Naming ◽  
Correlation Filter ◽  
Data Set ◽  
Scale Estimation ◽  
Searching Strategy ◽  
Multiple Feature ◽  
Pixel Value

The core part of the popular tracking-by-detection trackers is the discriminative classifier, which distinguishes the tracked target from the surrounding environment. Correlation filter-based visual tracking methods have the advantage of computing efficiency over the traditional methods by exploiting the properties of circulant matrix in learning process, and the significant progress in efficiency has been achieved by making use of the fast Fourier transform at detection and learning stages. But most existing correlation filter-based approaches are mainly restricted to translation estimation, which are susceptible to drifting in long-term tracking. In this article, a compressed multiple feature and adaptive scale estimation method is presented, which uses multiple features, including histogram of orientation gradients, color-naming, and raw pixel value to further improve the stability and accuracy of translation estimation. And for the scale estimation, another correlation filter is trained, which uses the compressed histogram of orientation gradients and raw pixel value to construct a multiscale pyramid of the target, and the optimal scale is obtained by exhaustively searching. The translation and scale estimation are unified with an iterative searching strategy. Extensively experimental results on the benchmark data set of scale variation show that the performance of the proposed compressed multiple feature and adaptive scale estimation algorithm is competitive against state-of-the-art methods with scale estimation capabilities in terms of robustness and accuracy.

Sign in / Sign up

Export Citation Format

Share Document