scholarly journals A Hybrid Surf-Based Tracking Algorithm with Online Template Generation

2019 ◽  
Vol 8 (12) ◽  
pp. 794-798
Keyword(s):  
Computer Vision ◽  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Mean Shift ◽  
Data Sets ◽  
Camera Motion ◽  
Interest Point ◽  
Illumination Change ◽  
Mean Shift Algorithm ◽  
Pose Variation

Visual tracking is the most challenging fields in the computer vision scope. Occlusion full or partial remains to be a big mile stone to achieve .This paper deals with occlusion along with illumination change, pose variation, scaling, and unexpected camera motion. This algorithm is interest point based using SURF as detector descriptor algorithm. SURF based Mean-Shift algorithm is combined with Lukas-Kanade tracker. This solves the problem of generation of online templates. These two trackers over the time rectify each other, avoiding any tracking failure. Also, Unscented Kalman Filter is used to predict the location of target if target comes under the influence of any of the above mentioned challenges. This combination makes the algorithm robust and useful when required for long tenure of tracking. This is proven by the results obtained through experiments conducted on various data sets.

scholarly journals A High-Precision, Real-Time, and Robust Indoor Visible Light Positioning Method Based on Mean Shift Algorithm and Unscented Kalman Filter

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1094 ◽  
Author(s):  
Zekun Xie ◽  
Weipeng Guan ◽  
Jieheng Zheng ◽  
Xinjie Zhang ◽  
Shihuan Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Visible Light ◽  
Real Time ◽  
High Speed ◽  
Unscented Kalman Filter ◽  
Light Emitting Diode ◽  
Mean Shift ◽  
Indoor Navigation ◽  
Positioning Accuracy ◽  
Mean Shift Algorithm

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.

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.

Study of Missile Radiator Predicting and Tracking Technology on the Basis of Kalman Filter and Mean-Shift Algorithm

2011 ◽  
Vol 383-390 ◽  
pp. 1584-1589
Author(s):  
Zhen Hui Xu ◽  
Bao Quan Mao ◽  
Li Xu ◽  
Jun Yan Zhao
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Kalman Filtering ◽  
Mean Shift ◽  
Moving Target ◽  
Tracking Method ◽  
The Real ◽  
Mean Shift Algorithm ◽  
Compensation Function ◽  
Tracking Technology

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.

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
Keyword(s):  
Kalman Filter ◽  
Tracking System ◽  
Mean Shift ◽  
Color Space ◽  
Image Sequence ◽  
Experimental Result ◽  
Dynamic Scene ◽  
Hsv Color Space ◽  
Mean Shift Algorithm ◽  
Camshift Algorithm

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.

Applied-Information Technology in Adaptive Kalman Filter with Model Error and Noise Error

2014 ◽  
Vol 1046 ◽  
pp. 380-383
Author(s):  
Juan Wang ◽  
Wei Wei Tao ◽  
Chun Ying Wu
Keyword(s):  
Kalman Filter ◽  
Target Location ◽  
Mean Shift ◽  
Model Error ◽  
Adaptive Kalman Filter ◽  
Current Frame ◽  
Mean Shift Algorithm ◽  
System Equation ◽  
Previous Frame ◽  
Noise Error

Kalman filter is successfully used to predict the object position under occlusion in this paper. Firstly, according to the target location in the previous frame, Kalman filter predicts target location in the current frame adaptively.Secondly, find the real target location in the neighborhood by mean shift algorithm. Finally, update the filter parameters. Because the adaptive Kalman filter predicts target location through system equation, it can improve the tracking effect in occlusion in a certain degree.

scholarly journals Implementation of Camshift Target Tracking Algorithm Based on Hybrid Filtering and Multifeature Fusion

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Sijie Du ◽  
Hongxin Xu ◽  
Tianping Li
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Low Cost ◽  
Mean Shift ◽  
Video Tracking ◽  
Experimental Result ◽  
Tracking Accuracy ◽  
Mean Shift Algorithm ◽  
Positioning Errors ◽  
Tracking Process

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.

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