Robust Spatial-Color Feature with New Similarity Measure and Adaptive Template Update for Mean-Shift Tracking

2013 ◽  
Vol 321-324 ◽  
pp. 1021-1029
Author(s):  
Lu Rong Shen ◽  
Xia Bin Dong ◽  
Rui Tao Lu ◽  
Yong Bin Zheng ◽  
Xin Sheng Huang
Keyword(s):  
Object Tracking ◽  
Similarity Measure ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Tracking Accuracy ◽  
Color Feature ◽  
Mean Shift Algorithm ◽  
Template Update ◽  
Update Strategy

In this paper, we analyze the object tracking task of mean-shift algorithm. A spatial-color and similarity based mean-shift tracking algorithm is proposed. The spatial-color feature is used to replace the color histogram, and an enhanced algorithm is derived by adopting a new similarity measure. We also introduce Lucas-Kanade algorithm to design a template update strategy, propose a template update algorithm for mean-shift. Experimental results show that these two improved mean-shift tracking algorithms have high tracking accuracy and good robustness to the change of appearance of the object.

Tracking algorithm of snowboard target in intelligent system

2020 ◽  
pp. 1-9
Author(s):  
Zhipeng Li ◽  
Xiaolan Li ◽  
Ming Shi ◽  
Wenli Song ◽  
Guowei Zhao ◽  
...  
Keyword(s):  
Target Tracking ◽  
High Speed ◽  
Intelligent System ◽  
Mean Shift ◽  
Target Position ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Mean Shift Algorithm ◽  
Target Track ◽  
The Mean

Snowboarding is a kind of sport that takes snowboarding as a tool, swivels and glides rapidly on the specified slope line, and completes all kinds of difficult actions in the air. Because the sport is in the state of high-speed movement, it is difficult to direct guidance during the sport, which is not conducive to athletes to find problems and correct them, so it is necessary to track the target track of snowboarding. The target tracking algorithm is the main solution to this task, but there are many problems in the existing target tracking algorithm that have not been solved, especially the target tracking accuracy in complex scenes is insufficient. Therefore, based on the advantages of the mean shift algorithm and Kalman algorithm, this paper proposes a better tracking algorithm for snowboard moving targets. In the method designed in this paper, in order to solve the problem, a multi-algorithm fusion target tracking algorithm is proposed. Firstly, the SIFT feature algorithm is used for rough matching to determine the fuzzy position of the target. Then, the good performance of the mean shift algorithm is used to further match the target position and determine the exact position of the target. Finally, the Kalman filtering algorithm is used to further improve the target tracking algorithm to solve the template trajectory prediction under occlusion and achieve the target trajectory tracking algorithm design of snowboarding.

Target Scale Adaptation Based on Corner Registration

2014 ◽  
Vol 556-562 ◽  
pp. 4260-4263
Author(s):  
Bing Yun Dai ◽  
Hui Zhao ◽  
Zheng Xi Kang
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Distance Ratio ◽  
Scale Adaptation ◽  
Mean Shift Algorithm

Target tracking algorithm mean-shift and kalman filter does well in tracking target. However, mean-shift algorithm may not do well in tracking the target which the size of target is changing gradually. Although some scholars put forward by 10% of the positive and negative incremental to scale adaptive,the algorithm can not be applied to track the target which gradually becomes bigger. In this paper, we propose registration corners of the target of the two adjacent frames, then calculate the distance ratio of registration corners.Use the distance ratio to determine the target becomes larger or smaller. The experimental results demonstrate that the proposed method performs better compared with the recent algorithms.

scholarly journals Robust Online Object Tracking Based on Feature Grouping and 2DPCA

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Ming-Xin Jiang ◽  
Jun-Xing Zhang ◽  
Min Li
Keyword(s):  
Object Tracking ◽  
Principal Component ◽  
Subspace Learning ◽  
Tracking Algorithm ◽  
Posteriori Probability ◽  
Error Matrix ◽  
A Posteriori Probability ◽  
Template Update ◽  
Feature Grouping ◽  
Update Strategy

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.

scholarly journals Object Tracking Algorithm Based on Dual Color Feature Fusion with Dimension Reduction

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 73 ◽  
Author(s):  
Shuo Hu ◽  
Yanan Ge ◽  
Jianglong Han ◽  
Xuguang Zhang
Keyword(s):  
Dimension Reduction ◽  
Object Tracking ◽  
Feature Fusion ◽  
Target Position ◽  
Input Image ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Color Feature ◽  
Color Distribution ◽  
Dual Color

Aiming at the problem of poor robustness and the low effectiveness of target tracking in complex scenes by using single color features, an object-tracking algorithm based on dual color feature fusion via dimension reduction is proposed, according to the Correlation Filter (CF)-based tracking framework. First, Color Name (CN) feature and Color Histogram (CH) feature extraction are respectively performed on the input image, and then the template and the candidate region are correlated by the CF-based methods, and the CH response and CN response of the target region are obtained, respectively. A self-adaptive feature fusion strategy is proposed to linearly fuse the CH response and the CN response to obtain a dual color feature response with global color distribution information and main color information. Finally, the position of the target is estimated, based on the fused response map, with the maximum of the fused response map corresponding to the estimated target position. The proposed method is based on fusion in the framework of the Staple algorithm, and dimension reduction by Principal Component Analysis (PCA) on the scale; the complexity of the algorithm is reduced, and the tracking performance is further improved. Experimental results on quantitative and qualitative evaluations on challenging benchmark sequences show that the proposed algorithm has better tracking accuracy and robustness than other state-of-the-art tracking algorithms in complex scenarios.

Mean-Shift Guided Particle Filter Tracking Method Based on Layered Dynamic Template Update

2013 ◽  
Vol 846-847 ◽  
pp. 1217-1220
Author(s):  
Yuan Zheng Li
Keyword(s):  
Particle Filter ◽  
Mean Shift ◽  
Tracking Method ◽  
Mean Shift Algorithm ◽  
Template Update ◽  
Complex Scenes ◽  
One Step ◽  
The One ◽  
Dynamic Template ◽  
Update Strategy

Traditional tracking algorithm is not compatible between robustness and efficiency, under complex scenes, the stable template update strategy is not robust to target appearance changes. Therefore, the paper presents a dynamic template-update method that combined with a mean-shift guided particle filter tracking method. By incorporating the original information into the updated template, or according to the variety of each component in template to adjust the updating weights adaptively, the presented algorithm has the natural ability of anti-drift. Besides, the proposed method cope the one-step iteration of mean-shift algorithm with the particle filter, thus boost the performance of efficiency. Experimental results show the feasibility of the proposed algorithm in this paper.

scholarly journals Visual Object Tracking Based on 2DPCA and ML

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Ming-Xin Jiang ◽  
Min Li ◽  
Hong-Yu Wang
Keyword(s):  
Object Tracking ◽  
Principal Component ◽  
Likelihood Estimation ◽  
Subspace Learning ◽  
Tracking Algorithm ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Error Matrix ◽  
Template Update ◽  
Update Strategy

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.

Object Tracking Based on Corrected Background-Weighted Histogram Mean Shift and Kalman Filter

2013 ◽  
Vol 765-767 ◽  
pp. 720-725 ◽  
Author(s):  
Yu Yang ◽  
Yong Xing Jia ◽  
Chuan Zhen Rong ◽  
Ying Zhu ◽  
Yuan Wang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Complex Environments ◽  
The Real ◽  
Real Environment ◽  
Background Clutter ◽  
Weighted Histogram

The classical mean shift (MS) algorithm is the best color-based method for object tracking. However, in the real environment it presents some limitations, especially under the presence of noise, objects with partial and full occlusions in complex environments. In order to deal with these problems, this paper proposes a reliable object tracking algorithm using corrected background-weighted histogram (CBWH) and the Kalman filter (KF) based on the MS method. The experimental results show that the proposed method is superior to the traditional MS tracking in the following aspects: 1) it provides consistent object tracking throughout the video; 2) it is not influenced by the objects with partial and full occlusions; 3) it is less prone to the background clutter.

Robust Object Tracking Based on Modified Mean-Shift Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 2738-2741
Author(s):  
Wei Li ◽  
Xing Wei Li ◽  
Rui Tao Lu ◽  
Dong Dong Zou
Keyword(s):  
Object Tracking ◽  
Mean Shift ◽  
Spatial Location ◽  
Experimental Results ◽  
Mean Shift Algorithm ◽  
Traditional Algorithm ◽  
Layered Approach ◽  
Spatial Histogram

In this article, aiming at the flaws in traditional Mean-shift algorithm for object tracking, we improve the traditional algorithm based on the fusion of Grayscale and Gradient using a layered approach. This algorithm can describe the object more exactly by using Grayscale and Gradient dual tracking factors in object tracking; And it makes up the flaws in traditional Mean-shift algorithm for information expression of spatial location by using layered approach and spatial histogram in describing object. Experimental results show that modified Mean-shift algorithm is superior to traditional Mean-shift algorithm in object tracking with better Robustness and precision, and it can suit deformation, spin, interference, shield and more complex tracking problems.

The Improvement of Mean - Shift Algorithm in the Video of Global Visual Robotic Fish in Tracking Moving Targets

2013 ◽  
Vol 475-476 ◽  
pp. 947-951
Author(s):  
Zhi Yuan Mai ◽  
Kun Yu Tan ◽  
An Ting Xu ◽  
Wei Xiang
Keyword(s):  
Real Time ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Robotic Fish ◽  
Experimental Results ◽  
Moving Targets ◽  
Mean Shift Algorithm ◽  
Good For ◽  
The Difference

The tracking effect is not good for the faster track with Mean Shift tracking algorithm when the difference is not obvious between the track target and background pixels in the video of global visual robotic fish.To solve the difficulty of tracking drastically moving targets in this paper, determining the position of moving targets in the next frame through comparing with two bc coefficients which have been set when the Epanechnikov has been selected core to estimate is indeed. The experimental results show the proposed algorithm can track the moving targets efficiently and precisely in video,and also can meet high real-time situation with small calculation.

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