Gesture Tracking and Locating Algorithm Based on Federated Tracking Filter

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Lei Yu ◽  
Junyi Hou ◽  
Shumin Fei
Keyword(s):  
Mean Shift ◽  
Experimental Simulation ◽  
Human Hand ◽  
Sampling Point ◽  
Tracking Accuracy ◽  
Mean Shift Algorithm ◽  
Gesture Tracking ◽  
Tracking Time ◽  
Tracking Filter ◽  
The Impact

Abstract In this paper, a joint gesture tracking method combining particle filter and mean shift algorithm is proposed to improve the accuracy and robustness of the system. During the slow movement of the human hand, the average movement of the particles is first used to cause most of the particles to drift into the gesture area. In the case where the movement of the human hand is faster or there is occlusion, when the mean shift of the particle is performed, if the region of the gesture is not detected, the particle will return to the state before the drift, and then the next frame is processed. The method can directly calculate the position of the gesture based on the particles used for subsequent testing, and can save the tracking time of the algorithm. Through experimental simulation, compared with the Cam-shift algorithm, when the sampling point of the joint tracking algorithm proposed in this paper is 200, the tracking accuracy is improved to 95.2%. Compared with 90.6% of the Cam-shift algorithm, the tracking time is reduced from 83.7 ms to 25.8 ms. Therefore, the proposed algorithm can greatly improve the tracking accuracy and real-time, and can also effectively reduce the impact of complex environments on the tracking effect.

Tracking algorithm of snowboard target in intelligent system

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.

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.

scholarly journals Real-Time Depth-Based Hand Detection and Tracking

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Sung-Il Joo ◽  
Sun-Hee Weon ◽  
Hyung-Il Choi
Keyword(s):  
Real Time ◽  
Mean Shift ◽  
Region Growing ◽  
Tracking Accuracy ◽  
Hand Detection ◽  
Seed Point ◽  
Mean Shift Algorithm ◽  
Detection And Tracking ◽  
Cascade Structure ◽  
Hand Region

This paper illustrates the hand detection and tracking method that operates in real time on depth data. To detect a hand region, we propose the classifier that combines a boosting and a cascade structure. The classifier uses the features of depth-difference at the stage of detection as well as learning. The features of each candidate segment are to be computed by subtracting the averages of depth values of subblocks from the central depth value of the segment. The features are selectively employed according to their discriminating power when constructing the classifier. To predict a hand region in a successive frame, a seed point in the next frame is to be determined. Starting from the seed point, a region growing scheme is applied to obtain a hand region. To determine the central point of a hand, we propose the so-called Depth Adaptive Mean Shift algorithm. DAM-Shift is a variant of CAM-Shift (Bradski, 1998), where the size of the search disk varies according to the depth of a hand. We have evaluated the proposed hand detection and tracking algorithm by comparing it against the existing AdaBoost (Friedman et al., 2000) qualitatively and quantitatively. We have analyzed the tracking accuracy through performance tests in various situations.

Robust Spatial-Color Feature with New Similarity Measure and Adaptive Template Update for Mean-Shift Tracking

2013 ◽  
Vol 321-324 ◽  
pp. 1021-1029
Author(s):  
Lu Rong Shen ◽  
Xia Bin Dong ◽  
Rui Tao Lu ◽  
Yong Bin Zheng ◽  
Xin Sheng Huang
Keyword(s):  
Object Tracking ◽  
Similarity Measure ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Tracking Accuracy ◽  
Color Feature ◽  
Mean Shift Algorithm ◽  
Template Update ◽  
Update Strategy

In this paper, we analyze the object tracking task of mean-shift algorithm. A spatial-color and similarity based mean-shift tracking algorithm is proposed. The spatial-color feature is used to replace the color histogram, and an enhanced algorithm is derived by adopting a new similarity measure. We also introduce Lucas-Kanade algorithm to design a template update strategy, propose a template update algorithm for mean-shift. Experimental results show that these two improved mean-shift tracking algorithms have high tracking accuracy and good robustness to the change of appearance of the object.

An Improved Mean Shift Algorithm for Vehicle Tracking

2013 ◽  
Vol 718-720 ◽  
pp. 2329-2334
Author(s):  
Yan Shuang Hao ◽  
Yi Xin Yin ◽  
Jin Hui Lan ◽  
Shu Wei Xiao
Keyword(s):  
Mean Shift ◽  
Initial Position ◽  
Tracking Algorithm ◽  
Vehicle Tracking ◽  
Tracking Accuracy ◽  
Shift Method ◽  
Mean Shift Algorithm ◽  
Fast Change ◽  
Position Prediction ◽  
Auto Regression

Classical mean shift tracking algorithm doesnt show good performance when the tracked objects move fast, change in size or pose. This paper proposes an improved mean shift method used for vehicle tracking. Firstly, a position prediction model based on second order auto-regression process is used to find the initial position of mean shift iteration, reduce times of iteration and enhance the tracking accuracy. Secondly, we employ a position search method based on the weight image to improve the tracking result when the result of basic mean shift tracking is not good. The proposed algorithm is tested in a real traffic video to track a vehicle changing in size and pose with more accurate result than basic mean shift tracking algorithm.

Automatic vertebra segmentation based on graph cut and mean shift algorithm

2011 ◽  
Vol 31 (3) ◽  
pp. 760-762
Author(s):  
Ji LIU ◽  
Xiao-dong KANG ◽  
Fu-cang JIA
Keyword(s):  
Mean Shift ◽  
Graph Cut ◽  
Mean Shift Algorithm

“From Tool to Agent”: The Evolution of (Cockpit) Automation and Its Impact on Human-Machine Coordination

1995 ◽  
Vol 39 (1) ◽  
pp. 79-83 ◽  
Author(s):  
Nadine B. Sarter ◽  
David D. Woods
Keyword(s):  
Experimental Simulation ◽  
Early Generation ◽  
Timely Feedback ◽  
Transition Training ◽  
Glass Cockpit ◽  
And Behavior ◽  
Available Information ◽  
The Impact ◽  
Communicative Abilities ◽  
Frequent Problem

In a variety of domains, researchers have observed breakdowns in human-automation coordination and cooperation. One form of breakdown is a lack of mode awareness which can result in ‘automation surprises’. These are, in part, related to a lack of adequate feedback on system status and behavior. The need for effective and timely feedback has become even more pressing with the evolution of systems that operate at increasingly high levels of authority and autonomy. In the absence of improved feedback design, however, the gap between required and available information has widened. To explore the impact of this trend towards ‘strong yet silent’ machine agents, a line of research was conducted on pilot-automation coordination on the Airbus A-320, an aircraft that exemplifies these trends. This research involved a survey of pilots' line experiences with the A-320 automation, observations of transition training to the airplane, and an experimental simulation study on pilots' mode awareness and pilot-automation coordination. The results of this work indicate a trend from mode errors of commission (which represented a more frequent problem on early generation ‘glass cockpit’ aircraft) to errors of omission. In other words, pilots were more likely to fail to observe and interfere with uncommanded and undesired automation and aircraft behavior. Such errors of omission also seem to have played a role in recent incidents and accidents. They illustrate the need for improved communicative abilities in autonomous and powerful systems to enable them to actively support the coordination between human and machine.

Sign in / Sign up

Export Citation Format

Share Document