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Robust tracking is an important and challenging problem in computer vision. Most existing algorithms do not work well if there are confusing objects in the surrounding environment or the target appearance has a significant change. This paper describes a novel particle filter for object tracking. First, we treat the blob image of the object as a matrix and adopt singular values to construct the feature...
People have a growing interest for driver assistant systems that are used to monitor the driving conditions by visual technique, and warn and guide drivers the road conditions. This paper proposes a real-time lane detection algorithm which is a necessary part for driver assistant system and unmanned vehicle. The algorithm presented in this paper integrates multiple cues, including bar filter which...
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...
Visual object tracking is an important topic in multimedia technologies. This paper presents robust implementation of an object tracker using a vision system that takes into consideration partial occlusions, rotation and scale for a variety of different objects. A scale invariant feature transform (SIFT) based color particle filter algorithm is proposed for object tracking in real scenarios. The Scale...
The problem of object tracking in dense clutter is a challenge in computer vision. This paper proposes a method for tracking object robustly by combining the online selection of discriminative color features and the offline selection of discriminative Haar features. Furthermore, the cascade particle filter which has four stages of importance sampling is used to fuse two kinds of features efficiently...
An object tracking algorithm based on the particle filter framework is proposed in this paper for video surveillance applications. The color histogram is combined with a scale invariant feature transform (SIFT) descriptor to represent the likelihood between the candidates and observed objects. They are then incorporated into the particle filter based tracking algorithm in order to achieve more robust...
This paper proposed a multi-cue based face tracking algorithm with the help of parallel multi-core processing. Due to illumination and occlusion problems, face tracking usually does not work stably based on a single cue. Three different visual cues, color histogram, edge orientation histogram and wavelet feature, are integrated under the framework of particle filter to improve the tracking performance...
Aiming at the problem of real time and robustness of visual object tracking in a clutter background, an adaptive fusion of color rectangle feature and edge strength local mean tracking algorithm based on particle filter is put forward. To improve the tracking speed and precision, integral image is used to quickly compute the color rectangle feature and edge strength local mean, besides fuzzy logic...
Robust real-time tracking of non-rigid objects is a challenging task. Color is a powerful feature for tracking deformable objects in image sequences with complex backgrounds. Color distribution is applied, as it is robust to partial occlusion, is rotation and scale invariant and computationally efficient. Particle filter has been proven very successful for non-linear and non-Gaussian estimation tracking...
Robust and real time moving object tracking is a tricky job in computer vision problems. Particle filtering has been proven very successful for non-Gaussian and non-linear estimation problems. In this paper, we first try to develop a color based particle filter. In this approach, the object tracking system relies on the deterministic search of window, whose color content matches a reference histogram...
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