scholarly journals Target Contour Recovering for Tracking People in Complex Environments

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Jianhua Zhang ◽  
Sheng Liu ◽  
Y. F. Li ◽  
Jianwei Zhang

Recovering people contours from partial occlusion is a challenging problem in a visual tracking system. Partial occlusions would bring about unreasonable contour changes of the target object. In this paper, a novel method is presented to detect partial occlusion on people contours and recover occluded portions. Unlike other occlusion detection methods, the proposed method is only based on contours, which makes itself more flexible to be extended for further applications. Experiments with synthetic images demonstrate the accuracy of the method for detecting partial occlusions, and experiments on real-world video sequence are also carried out to prove that the method is also good enough to be used to recover target contours.

2012 ◽  
Vol 21 (01) ◽  
pp. 1250012 ◽  
Author(s):  
HUCHUAN LU ◽  
SHIPENG LU ◽  
GANG YANG

In this paper, we present a novel method for eye tracking, in detail describing the eye contour and the visible iris center. Combining the IVT (Incremental Visual Tracking) tracker, the proposed online affine manifold model, in which the sequentially learning shape and texture are modeled in the first stage and noniterative recovering estimation in the second stage, tracks the eye contour in video sequences. After that, an adaptive black round mask is generated to match the visible iris center. Experimental results of eye tracking indicate that our tracker works well in the PC or domestic camera captured image streams with considerable head and eyeball rotation.


Author(s):  
Muhammad Muazzam Hussain ◽  
Kashif Fahim ◽  
Arslan Majid

When entering into the realm of Computer Vision, the first thing which comes in to mind is Visual tracking. Visual tracking by far comes into one of the most actively investigated research areas because of the fact that it has an extensive collection of applications in areas such as activity recognition, surveillance, motion analysis and as well as human computer interaction. Some serious challenges of this area which still create hindrance in achieving 100% accuracy are abrupt appearance and pose changes of an object along with its background blockage due to blockages called occlusion, illumination and lighting variances and changes in scale of target object in the frames. Moreover, diverse algorithms had been proposed for the resolution of said issue. Now in such cases, if we study the statistical analysis of correlation between two frames in a certain video, it can be efficiently utilized to get the most exact location of the targeted object. The algorithms in existence today do not completely exploit a strong spatio-temporal relationship that very often occurs between the two successive frames in a video sequence. Recent advances in correlation-based tracking systems have been proposed to address the problem in successive frames. In this thesis a very simple yet quite speedy and robust algorithm that in actual brings all the relevant information used for Visual Tracking. Two of the Models proposed are the “Locality Sensitive Histogram” and “Discriminative Scale Tracking Method”. These are robust enough to the variations which are based on appearance which are normally presented by blockage, pose, illumination and lighting variations alike. A scheme is proposed called scale adaptation which is very much clever to adapt variations of targeted scale in the most efficient manner. The Discriminative Scale Tracking Method is used for detection as well as scale change ultimately resulting in an effective tracking method in the end. Various different experiments with the best algorithms have demonstrated on challenging sequences that the suggested methodology attains promising results as far as robustness, accuracy, and speed is concerned.


2005 ◽  
Vol 37 (3) ◽  
pp. 453-463 ◽  
Author(s):  
Zia Khan ◽  
Rebecca A. Herman ◽  
Kim Wallen ◽  
Tucker Balch

2018 ◽  
Vol 246 ◽  
pp. 03020
Author(s):  
Tan Wei ◽  
Xuan Liu ◽  
Chen Yi ◽  
Erfu Yang

With the development of industrial automation, location measurement of 3D objects is becoming more and more important, especially as it can provide necessary positional parameters for the manipulator to grasp the object accurately. In view of the disabled object which is in widespread use currently, its image is captured to obtain positional parameters and transmitted to manipulators in industry. The above process is delayed, affecting the work efficiency of the manipulator. A method for calculating the position information of target object in motion is proposed. This method uses monocular vision technology to track 3D moving objects,then uses contour sorting method to extract the minimum constrained contour rectangle, and combines the video alignment technology to realize the tracking. Thus, the measurement error is reduced. The experimental results and analysis show that the adopted measurement method is effective.


2009 ◽  
Author(s):  
Zai Jian Jia ◽  
Tomás Bautista ◽  
Antonio Núñez ◽  
Cayetano Guerra ◽  
Mario Hernández

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