The Target Tracking Method Based on Camshift Algorithm Combined with SIFT

Since Camshift algorithm leads to failed tracking results when the color information of the target region is similar with the background or is not precise enough, to solve this problem a tracking method based on Camshift and SIFT was proposed in this paper. In this method, SIFT feature points, which were used to construct the color histogram and the color probability distribution, were extracted from the target region first. Then SIFT points were also extracted from the search region and these two sets of SIFT points were matched. Since the proposed method used the matched SIFT points to properly guide the location of targets, experimental results show that with the new method some more accurate and robust tracking results have been obtained.

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An Improved Trace Method for Moving Target under Occlusion

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.672-674.1931 ◽

2014 ◽

Vol 672-674 ◽

pp. 1931-1934

Author(s):

Yu Bing Dong ◽

Guang Liang Cheng ◽

Ming Jing Li

Keyword(s):

Mathematical Model ◽

Target Tracking ◽

Difficult Problem ◽

Block Matching ◽

Experimental Results ◽

Polar Coordinates ◽

Moving Target ◽

Trajectory Prediction ◽

Tracking Method

Occlusion is a difficult problem to be solved in the process of target tracking. In order to solve the problem of occlusion, a new tracking method combined with trajectory prediction and multi-block matching is presented and studied,and a mathematical model of trajectory prediction of moving target is established in polar coordinates and verified through some experiments. The experimental results show that the new tracking method can be better to trace and forecast the moving target under occlusion.

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Mean Shift-Based Target Tracking for Moving Spherical Object in Video

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.623.156 ◽

2014 ◽

Vol 623 ◽

pp. 156-160

Author(s):

Bo Zhao Li

Keyword(s):

Target Tracking ◽

Mean Shift ◽

Feature Points ◽

Color Information ◽

Spherical Object ◽

Color Feature ◽

Color Distribution ◽

Distribution Probability ◽

Flow Algorithm ◽

Tracking Object

When researching on target tracking in the on or off line video, using a variety of methods such as MeanShift, Camshaft’s, feature points and optical flow algorithm. MeanShift target tracking algorithm is introduced in this paper. Firstly, tracking object is selected by human-computer interaction. Then color feature histogram is obtained using RGB color information, and color distribution probability image is got by converting color feature histogram. Finally, by comparing the probability difference of color distribution of the adjacent frames, motion directions of the object’s center are obtained, which object can be effectively tracked.

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A Face Tracking Method Based on Camshift and SCKF in Rapid Moving Process

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.668-669.1025 ◽

2014 ◽

Vol 668-669 ◽

pp. 1025-1028

Author(s):

Fu Cheng Cao ◽

Xiao Xue Xing

Keyword(s):

Nonlinear Effect ◽

Face Tracking ◽

Uniform Motion ◽

Square Root ◽

Robust Tracking ◽

Tracking Accuracy ◽

Tracking Method ◽

Camshift Algorithm ◽

Moving Process ◽

Localization And Tracking

Aiming at the problem of face tracking under rapid moving process, a fast and robust tracking method is proposed. The possible position of face detected by the Camshift algorithm in the next frame is predicted by the square-root cubature Kalman filte (SCKF). Then, the localization and tracking of face are got frames by frames. The experimental results show that: the use of SCKF to solve the nonlinear effect caused by non-uniform motion of face and overcome the target loss problem of the linear Kalman algorithm. The proposed method greatly improves the tracking accuracy of face in the process of rapid movement.

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An Improved Algorithm on Adaptive KLT Vision Tracking

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.631-632.1270 ◽

2013 ◽

Vol 631-632 ◽

pp. 1270-1275

Author(s):

Yuan Min Liu ◽

Lian Fang Tian

Keyword(s):

State Of The Art ◽

Tracking System ◽

Tracking Algorithm ◽

Experimental Results ◽

Feature Points ◽

Backward Error ◽

Tracking Method ◽

Adaptive Tracking ◽

Conventional Methods ◽

Measurement Validity

In view of the shortage of the KLT (Kanade-Lucas-Tomasi) tracking algorithm, an improved adaptive tracking method based on KLT is proposed in this paper, in which a kind of filtering mechanism is applied to decrease the effects of noise and illumination on tracking system. In order to eliminate the error of tracking, the strategies based on forward-backward error and measurement validity are utilized properly. However, because the approach to forward-backward error makes the feature points reduce, which leads to tracking failure especially when the shapes of object change, a method for appending the feature points is introduced. Experimental results indicate that the method presented in this paper is state of the art robustness in our comparison with related work and demonstrate improved generalization over the conventional methods.

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A Target Tracking Method Based on Improved Camshift Algorithm

2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) ◽

10.1109/itoec49072.2020.9141593 ◽

2020 ◽

Author(s):

Jisheng Nie ◽

Xu Guo

Keyword(s):

Target Tracking ◽

Tracking Method ◽

Camshift Algorithm

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Probabilistic Background Model by Density Forests for Tracking

International Journal of Software Innovation ◽

10.4018/ijsi.2017040101 ◽

2017 ◽

Vol 5 (2) ◽

pp. 1-16

Author(s):

Daimu Oiwa ◽

Shinji Fukui ◽

Yuji Iwahori ◽

Tsuyoshi Nakamura ◽

Boonserm Kijsirikul ◽

...

Keyword(s):

Likelihood Function ◽

Target Object ◽

Image Sequences ◽

Background Model ◽

Robust Tracking ◽

Color Information ◽

Background Region ◽

Previous Approach ◽

Tracking Method ◽

Outdoor Scenes

This paper proposes an approach for a robust tracking method to the objects intersection with appearances similar to a target object. The target is image sequences taken by a moving camera in this paper. Tracking methods using color information tend to track mistakenly a background region or an object with color similar to the target object since the proposed method is based on the particle filter. The method constructs the probabilistic background model by the histogram of the optical flow and defines the likelihood function so that the likelihood in the region of the target object may become large. This leads to increasing the accuracy of tracking. The probabilistic background model is made by the density forests. It can infer a probabilistic density fast. The proposed method can process faster than the authors' previous approach by introducing the density forests. Results are demonstrated by experiments using the real videos of outdoor scenes.

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Multi-target tracking method with flexibility of illumination change

2016 IEEE International Conference on Consumer Electronics-China (ICCE-China) ◽

10.1109/icce-china.2016.7849746 ◽

2016 ◽

Author(s):

Yinrong Wu ◽

Jianxiu Jin ◽

Xiaofen Xing

Keyword(s):

Target Tracking ◽

Illumination Change ◽

Tracking Method ◽

Multi Target Tracking

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Robust Autonomous Car-Like Robot Tracking Based on Tracking-Learning-Detection

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.564 ◽

2014 ◽

Vol 687-691 ◽

pp. 564-571 ◽

Cited By ~ 1

Author(s):

Lin Bao Xu ◽

Shu Ming Tang ◽

Jin Feng Yang ◽

Yan Min Dong

Keyword(s):

Optical Flow ◽

Fast Moving ◽

Moving Objects ◽

Tracking Algorithm ◽

Robust Tracking ◽

Symmetry Detection ◽

Tracking Method ◽

Robot Tracking ◽

Improve Accuracy ◽

First Time

This paper proposes a robust tracking algorithm for an autonomous car-like robot, and this algorithm is based on the Tracking-Learning-Detection (TLD). In this paper, the TLD method is extended to track the autonomous car-like robot for the first time. In order to improve accuracy and robustness of the proposed algorithm, a method of symmetry detection of autonomous car-like robot rear is integrated into the TLD. Moreover, the Median-Flow tracker in TLD is improved with a pyramid-based optical flow tracking method to capture fast moving objects. Extensive experiments and comparisons show the robustness of the proposed method.

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