An Adaptive Tracking Algorithm Based on Mean Shift

Object tracking is to search the most similar parts to targets in video sequences. Among the various tracking algorithms, mean shift tracking algorithm has become popular due to its simplicity, efficiency and good performance. This paper focused on mean shift tracking algorithm, which is a modeling mechanism based on statistical probability density function. In practice, when the background of the tracking and characteristics of the target are similar, pixels of background occupy a large proportion in the histogram. The traditional mean shift cannot adapt to the mutative scene. Meanwhile, if there is block or disappearance, the result is not exact. Three algorithms were given for above difficulties. A weighted template background was established, that can highlight the features of target and improve real-time. Then this paper presented a selective mechanism to update the target model. Every component is updated based on the contribution to the target model. Finally, the Kalman filter was combined with mean shift algorithm. We saw the prediction points of Kalman filter as the initial point, carried out the mean shift iteration and then updated Kalman filter using the ultimate location. Extensive experimental results illustrated excellent agreement with these methods.

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Tracking algorithm of snowboard target in intelligent system

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189350 ◽

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.

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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 Analysis Based on Corner Registration

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.760-762.1997 ◽

2013 ◽

Vol 760-762 ◽

pp. 1997-2001

Author(s):

Zheng Xi Kang ◽

Hui Zhao ◽

Yuan Zhen Dang

Keyword(s):

Kalman Filter ◽

Theoretical Analysis ◽

Target Tracking ◽

Affine Transformation ◽

Mean Shift ◽

Tracking Algorithm ◽

Transformation Theory ◽

Mobile Target ◽

The Mean ◽

Initial Iteration

Target tracking algorithm based on Mean-Shift and Kalman filter does well in linear tracking. However, the algorithm might lose the target when the trace of mobile target is curve or the acceleration is not constant. To cope with these drawbacks, this paper proposes Target Tracking Analysis Based on Corner Registration. The algorithm modifies the initial iteration center of Mean-Shift by using the corner features combined with affine transformation theory and then the Mean-Shift can track the target. The theoretical analysis and the experimental results demonstrate that this method can overcome the drawbacks we talk above and make achievements in target tracking.

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Robust object tracking via online discriminative appearance modeling

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-019-0646-0 ◽

2019 ◽

Vol 2019 (1) ◽

Author(s):

Wei Liu ◽

Xin Sun ◽

Dong Li

Keyword(s):

Object Tracking ◽

Spatial Information ◽

State Of The Art ◽

Mean Shift ◽

Video Sequences ◽

Appearance Modeling ◽

Target Region ◽

Target Model ◽

The Mean ◽

Candidate Regions

Abstract A robust object tracking algorithm is proposed in this paper based on an online discriminative appearance modeling mechanism. In contrast with traditional trackers whose computations cover the whole target region and may easily be polluted by the similar background pixels, we divided the target into a number of patches and take the most discriminative one as the tracking basis. With the consideration of both the photometric and spatial information, we construct a discriminative target model on it. Then, a likelihood map can be got by comparing the target model with candidate regions, on which the mean shift procedure is employed for mode seeking. Finally, we update the target model to adapt to the appearance variation. Experimental results on a number of challenging video sequences confirm that the proposed method outperforms the related state-of-the-art trackers.

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A Fast Object Tracking Approach in Vision Application

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.3265 ◽

2014 ◽

Vol 513-517 ◽

pp. 3265-3268

Author(s):

Xiao Jing Zhang ◽

Chen Ming Sha ◽

Ya Jie Yue

Keyword(s):

Virtual Reality ◽

Object Tracking ◽

Mean Shift ◽

Estimation Method ◽

Parameters Estimation ◽

Video Sequences ◽

Application Area ◽

Mean Shift Algorithm ◽

The Mean ◽

Vision Application

Object tracking has always been a hot issue in vision application, its application area include video surveillance, human-machine, virtual reality and so on. In this paper, we introduce the Mean shift tracking algorithm, which is a kind of important no parameters estimation method, then we evaluate the tracking performance of Mean shift algorithm on different video sequences.

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A New Modeling Method for Gray Feature

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.3814 ◽

2014 ◽

Vol 556-562 ◽

pp. 3814-3817

Author(s):

Ming Li ◽

Chao Chen

Keyword(s):

Mean Shift ◽

Modeling Method ◽

Feature Modeling ◽

Object Model ◽

Complex Environment ◽

Value Differences ◽

Mean Shift Algorithm ◽

Target Model ◽

The Mean ◽

Partially Occluded

The Mean-Shift algorithm has very good tracking effect when the background is in a simple; but for a complex environment, tracking effect is not very ideal. Therefore, a new gray feature modeling method is proposed in this paper. Firstly, target in the tracking window is uniformly divided into even pieces. Then the pixel gray value of each block is calculated with subtraction of certain rules. Finally, the gray value of gray difference and the whole object value fusion are fused and established the object model. The object model that established not only contains the whole gray value information, but also contains the gray value differences between regions, has a more accurate description of the target, and then distinguish target from background better. The experiment results show that: the target model using the method in this paper to track based on the Mean-Shift algorithm, has good adaptability when the target is partially occluded and has better robustness for complex background.

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