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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...
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...
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...
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...
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