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

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.

The Target Tracking Method Based on Camshift Algorithm Combined with SIFT

2011 ◽  
Vol 186 ◽  
pp. 281-286 ◽  
Author(s):  
Jie Yu Zhang ◽  
Hai Yong Wu ◽  
Shu Chen ◽  
De Shen Xia
Keyword(s):  
Probability Distribution ◽  
Target Tracking ◽  
Experimental Results ◽  
Feature Points ◽  
Robust Tracking ◽  
Color Information ◽  
Search Region ◽  
Target Region ◽  
Tracking Method ◽  
Camshift Algorithm

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.

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

2014 ◽  
Vol 687-691 ◽  
pp. 564-571 ◽  
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.

Carrier Tracking Performance Comparison of Standards and the Square-Root Kalman Filter

2012 ◽  
Vol 239-240 ◽  
pp. 1188-1193
Author(s):  
Shao Jie Ni ◽  
Jing Pang ◽  
Xiao Mei Tang ◽  
Fei Xue Wang
Keyword(s):  
Kalman Filter ◽  
Engineering Design ◽  
Performance Comparison ◽  
Tracking Performance ◽  
Square Root ◽  
Signal Tracking ◽  
Tracking Method ◽  
High Carrier ◽  
Comparison Of Standards ◽  
Lock In

In order to solve the problem of loosing lock in weak GPS signal tracking, Kalman filter based carrier tracking method is presented.In this paper,two methods to track the GNSS carrier are compared,one is base on normal Kalman filter, another is based on square-root Kalman filter. The paper analyzes the under performance in the low carrier-to-noise ratio, and the expenditure of the actual project exists, but the high carrier to noise less discussed than the case will appear.The analyse and simulation result can be used to guide the engineering design of the GNSS receiver.

scholarly journals Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5549
Author(s):  
Ossi Kaltiokallio ◽  
Roland Hostettler ◽  
Hüseyin Yiğitler ◽  
Mikko Valkama
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Tracking System ◽  
Model Parameters ◽  
Calibration Data ◽  
Tracking Accuracy ◽  
Time Period ◽  
The Em Algorithm ◽  
Device Free ◽  
Localization And Tracking

Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was not occupied by people, or measurements while a person stands in known locations. Such calibration periods can be very expensive in terms of time and effort, making system deployment and maintenance challenging. This paper develops an Expectation-Maximization (EM) algorithm based on Gaussian smoothing for estimating the unknown RSS model parameters, liberating the system from supervised training and calibration periods. To fully use the EM algorithm’s potential, a novel localization-and-tracking system is presented to estimate a target’s arbitrary trajectory. To demonstrate the effectiveness of the proposed approach, it is shown that: (i) the system requires no calibration period; (ii) the EM algorithm improves the accuracy of existing DFLT methods; (iii) it is computationally very efficient; and (iv) the system outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy.

Research on Underwater Target Tracking Based on Contour Detection

2011 ◽  
Vol 317-319 ◽  
pp. 890-896
Author(s):  
Ming Jun Zhang ◽  
Yuan Yuan Wan ◽  
Zhen Zhong Chu
Keyword(s):  
Prediction Model ◽  
Target Tracking ◽  
Tracking System ◽  
Contour Detection ◽  
Motion Prediction ◽  
Moving Target ◽  
Contour Extraction ◽  
Tracking Accuracy ◽  
Tracking Method ◽  
Underwater Target

The traditional centroid tracking method over-relies on the accuracy of segment, which easily lead to loss of underwater moving target. This paper presents an object tracking method based on circular contour extraction, combining region feature and contour feature. Through the correction to circle features, the problem of multiple solutions causing by Hough transform circle detection is avoided. A new motion prediction model is constructed to make up the deficiency that three-order motion prediction model has disadvantage of high dimension and large calculation. The predicted position of object centroid is updated and corrected by circle contour, forming prediction-measurement-updating closed-loop target tracking system. To reduce system processing time, on the premise of the tracking accuracy, a dynamic detection method based on target state prediction model is proposed. The results of contour extraction and underwater moving target experiments demonstrate the effectiveness of the proposed method.

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