Improved infrared target-tracking algorithm based on mean shift

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.

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Target Scale Adaptation Based on Corner Registration

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.4260 ◽

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.

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Target tracking algorithm based on particle filter and mean shift under occlusions

2015 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) ◽

10.1109/icspcc.2015.7338848 ◽

2015 ◽

Author(s):

Li Zhanli ◽

Cui Leilei ◽

Xie Ailing

Keyword(s):

Particle Filter ◽

Target Tracking ◽

Mean Shift ◽

Tracking Algorithm

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The Development of Image Processing Board Based on Target Tracking

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.411-414.1322 ◽

2013 ◽

Vol 411-414 ◽

pp. 1322-1325

Author(s):

Ya Hui Hu ◽

Le Jiang Guo ◽

Xiao Lei ◽

Cheng Min

Keyword(s):

Image Processing ◽

Target Tracking ◽

Internal Structure ◽

Development Process ◽

Mean Shift ◽

Tracking Algorithm ◽

Algorithm Analysis ◽

Mean Shift Algorithm ◽

Specific Target ◽

Target Environment

This paper selects the target tracking algorithm suitable for specific target environment: using Mean Shift algorithm based on space edge direction histogram at initialization, selecting tracking algorithm based on block when there is a shelter. On the basis of algorithm analysis and software experiment and studying of TI Company's TMS320DM642 DSP chip internal structure and development process, these two algorithms researched in this paper were transplanted to DSP platform and a series of optimization were been made to the algorithms codes after transplanted ,implementing target tracking and identified via DSP development board instead of PC.

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Continuously Adaptive Mean-shift Tracking Algorithm Based on Improved Gaussian Model

Journal of Engineering Science and Technology Review ◽

10.25103/jestr.135.07 ◽

2020 ◽

Vol 13 (5) ◽

pp. 50-57

Author(s):

Jinping Sun ◽

◽

Enjie Ding ◽

Dan Li ◽

Kailiang Zhang ◽

...

Keyword(s):

Target Tracking ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Mean Shift ◽

Gaussian Mixture ◽

Tracking Algorithm ◽

Target Color ◽

Color Model ◽

Background Color ◽

Complex Scenes

In complex scenes with light changes, deformations, and occlusions, target tracking easily contains a large amount of background color information when building a target color model. Thus, the tracking effect is reduced. To improve the accuracy of the traditional continuously adaptive mean-shift algorithm (CAMShift) in complex scenarios, a target tracking algorithm based on an improved Gaussian mixture model was proposed. Using the Gaussian mixture model, the tracking image was divided into the foreground and background superposition. The histograms of the hue component were respectively established in the foreground and background of the target area. By suppressing the same hue as the background color in the tracking image, the target color model was established. The target position was iteratively obtained by implementing the CAMShift algorithm using the enhanced target color model. The Bhattacharyya distance between the candidate target and the target template was used as basis for updating the target model. Simulation analysis under benchmark data sets and actual monitoring scenarios verified the accuracy of the proposed algorithm. Results show that the distance precision and overlap success rate of the proposed algorithm are 0.88 and 0.625, respectively. The proposed algorithm effectively solves long-term target tracking problems with complex scenes, such as occlusion, background clutters, and illumination variation. This study eliminates the problem of target recognition caused by environmental changes and provides references for real-time monitoring of abnormal traffic conditions.

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