Object Tracking System in Dynamic Scene Based on Improved Camshift Algorithm and Kalman Filter

2014 ◽  
Vol 602-605 ◽  
pp. 2061-2064 ◽  
Author(s):  
Chao Bing Liu ◽  
Cong Cong Chen ◽  
Xiao Li

Camshift, namely "Continuously Adaptive Mean-Shift" algorithm, is an adaptive tracking algorithm. This algorithm is based on the color information to track the moving target in image sequence. In the simple background, this algorithm achieved a steady and current tracking effect. But in dynamic scene, the global motion caused by the camera, the background of the light and occlusion will affect the accuracy, or even lose the tracking of the target. In order to solve the above problem, this paper adjust the H component in HSV color space, as well use weighted color histogram to improve the Camshift algorithm, then combined with Kalman filter to track the target in the image sequence. The experimental result shows that this approach can track object stability and correctly in dynamic scene.

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Sijie Du ◽  
Hongxin Xu ◽  
Tianping Li

In recent years, the Mean shift algorithm has extensive applications in the field of video tracking. It has some advantages of low cost, small memory, and good tracking effect. However, there are some shortcomings in the existing algorithm; for example, it cannot produce adaptive changes as the target size changes. And when there are similar objects, it is prone to target positioning errors and tracking failures caused by occlusion. In this paper, an improved method of continuous adaptive change Mean shift (Camshift) for high-precision positioning and tracking is proposed. The traditional Camshift method only uses hue components in HSV to extract features. This paper uses the combination of H and S components in HSV space to build a two-dimensional color feature histogram and with the image’s LBP feature histogram to increase tracking accuracy. Meanwhile, for the sake of target occlusion and nonlinear changes in the tracking process, this paper introduces a Gaussian-Hermit particle filter that is updated by the Kalman filter. Experimental result demonstrates that the real-time performance of the proposal in this paper is better than Mean shift, Camshift, simple particle filter, and Kalman filter.


2010 ◽  
Vol 44-47 ◽  
pp. 3902-3906
Author(s):  
Jie Jia ◽  
Yong Jun Yang ◽  
Yi Ming Hou ◽  
Xiang Yang Zhang ◽  
He Huang

An object tracking framework based on adaboost and Mean-Shift for image sequence was proposed in the manuscript. The object rectangle and scene rectangle in the initial image of the sequence were drawn and then, labeled the pixel data in the two rectangles with 1 and 0. Trained the adaboost classifier by the pixel data and the corresponding labels. The obtained classifier was improved to be a 5 class classifier and employed to classify the data in the same scene region of next image. The confidence map including 5 values was got. The Mean-Shift algorithm is performed in the confidence map area to get the final object position. The rectangles of object and background were moved to the new position. The object rectangle was zoomed by 5 percent to adapt the object scale changing. The process including drawing rectangle, training, classification, orientation and zooming would be repeated until the end of the image sequence. The experiments result showed that the proposed algorithm is efficient for nonrigid object orientation in the dynamic scene.


2014 ◽  
Vol 556-562 ◽  
pp. 4260-4263
Author(s):  
Bing Yun Dai ◽  
Hui Zhao ◽  
Zheng Xi Kang

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.


2009 ◽  
Author(s):  
Hao Zhang ◽  
Jingxin Hong ◽  
Wu Lin ◽  
Lin Li

2011 ◽  
Vol 383-390 ◽  
pp. 1584-1589
Author(s):  
Zhen Hui Xu ◽  
Bao Quan Mao ◽  
Li Xu ◽  
Jun Yan Zhao

In order to improve the real-time character of missile radiator tracking and solve the predicting tracking problem when missile radiator shortly shelter or missing, it introduces moving target predicting and tracking technology. According to the predicting and tracking method, it proposes three predicting and tracking overall schemes of missile radiator based on Kalman filtering and improved Mean-Shift algorithm. Also it compares the real-time character of three kinds of schemes. According to the trajectory character of missile radiator, it constructs Kalman filter. The experiment results indicate that by using Kalman filtering technology, there are improvements in real-time character and shortly shelter or missing problem can be solved well. It plays a certain compensation function to the whole system.


Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1094 ◽  
Author(s):  
Zekun Xie ◽  
Weipeng Guan ◽  
Jieheng Zheng ◽  
Xinjie Zhang ◽  
Shihuan Chen ◽  
...  

Visible light positioning (VLP) is a promising technology for indoor navigation. However, most studies of VLP systems nowadays only focus on positioning accuracy, whereas robustness and real-time ability are often overlooked, which are all indispensable in actual VLP situations. Thus, we propose a novel VLP method based on mean shift (MS) algorithm and unscented Kalman filter (UKF) using image sensors as the positioning terminal and a Light Emitting Diode (LED) as the transmitting terminal. The main part of our VLP method is the MS algorithm, realizing high positioning accuracy with good robustness. Besides, UKF equips the mean shift algorithm with the capacity to track high-speed targets and improves the positioning accuracy when the LED is shielded. Moreover, a LED-ID (the identification of the LED) recognition algorithm proposed in our previous work was utilized to locate the LED in the initial frame, which also initialized MS and UKF. Furthermore, experiments showed that the positioning accuracy of our VLP algorithm was 0.42 cm, and the average processing time per frame was 24.93 ms. Also, even when half of the LED was shielded, the accuracy was maintained at 1.41 cm. All these data demonstrate that our proposed algorithm has excellent accuracy, strong robustness, and good real-time ability.


2021 ◽  
Author(s):  
ying Han ◽  
jing Yuan ◽  
qiao Wang ◽  
dehe Yang ◽  
xuhui Sun

<p>The power line harmonic generated by human activities can be found from the vast amount of the data observed by EFD on board the ZH-1 satellite. To study the human activities and remove the nonnegligible amount of interferences in the study of ionospheric precursors of earthquakes, we are desperate for finding the power line harmonic from the vast amount of data Hence, a novel automatic power line recognition method is proposed. Firstly, we utilize fourier transform on EFD data to obtain the power spectral density(PSD). Secondly, it is well known that harmonic radiation from power lines presents one or more horizontal linear characteristics on the PSD image and the color of the line is close to the color of the background in the image.In order to highlight the color contrast between the line and the background, we transform the PSD image from the RGB to the HSV color space and utilize the Saturation compoment of the HSV space as the object image.To obtain the edge regions, we process the object image with canny techniques. Finally, we use the Hough transform to detect the power line from the edge regions. To evaluate the proposed method, the experiment is performed for the dataset composed of 100 PSD images and each PSD image includes several interference lines. And the experimental result verifies the effectiveness of the proposed method with an accuracy of 86%.</p>


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