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Video object tracking is the process of locating one or several moving objects in time by the use of optical cameras. In this paper, an algorithm for object tracking by the use of particle filtering is presented. The algorithm employs fuzzy techniques for feature estimation. The algorithm handles color video image sequences from a stationary camera under changing illumination conditions. The proposed...
Object tracking based on color feature often fails in a complex background. To deal with this problem, a particle filtering object tracking approach is proposed in this paper based on local binary pattern and color feature. Color histogram is the global description of targets in color image, while local binary pattern texture contains information of neighbor region texture in gray image. These two...
In this paper, we propose a novel approach that combines particle filter tracking and 3D graph cut based segmentation to achieve silhouette tracking against drastic scale change and occlusion. The segmentation module offers particle filter tracking procedure the target shape information to compensate spatial information loss in the histogram based particle filter tracking process. Meanwhile, particle...
Moving object tracking has received much interest in the field of computer vision due to the increasing need for automated video analysis. Particles Filter is a very promising object tracking method since it is suitable for non-linear and/or non-Gaussian applications. Most particle filter applies color information in target model which might fail in the presence of similar colored objects in the scene...
Object tracking algorithm using modified Particle filter in low frame rate (LFR) video is proposed in this paper, which the object moving significantly and randomly between consecutive frames in the low frame rate situation. Traditionally, Particle filtering use motion transitions to model the movement of the target. However, in object tracking with low frame rate sequences, it is very difficult to...
A robust approach to detection and tracking of multiple moving targets from a moving camera is presented. The main novelty of this approach is that objects are represented using efficient compact form of the colour correlogram. Like previous correlograms, this encodes both spatial pattern and appearance information about the target. However it is less complex to compute, making it applicable to real...
A new algorithm for face tracking is proposed. Color information provides an effective cue for face tracking due to its robustness to scaling, rotation and translation. But the main limitation of color cue is that it can be easily interrupted by the camouflage objects that have the same or similar color with the target face. In order to achieve robust face tracking performance, the texture feature...
This paper describes an approach to tracking multiple independently moving objects observed from moving cameras. The method addresses difficulties typically associated with tracking, including changes in background, parallax in the scene, arbitrary camera motion, object occlusions, cross-overs, and appearance changes. Using a bottom up approach, independently moving objects are detected in images...
In this paper, we propose a boosted interactively distributed particle filter (BIDPF) to address the problem of automatic multi-object tracking in the application of player tracking in broadcast soccer video. The interactively distributed particle filter technique (IDPF) is adopted to handle the mutual occlusions among targets. The proposal distribution using a mixture model that incorporates information...
Particle filtering is an efficient and successful technique for tracking 2D and 3D motion through an image. We present the enhanced tracking of two hands based on a statistical model using only a skin colour feature with particle filtering for gesture recognition. Our framework employs one particle filter per hand individually with the pixel-wise classification of the likelihood of the skin in the...
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