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Object tracking is still remains as a challenge to the computer vision community. Several methods have been proposed until now to track. One renowned method to track is particle filter, a probabilistic model that predicts object position based on recursive Bays formula. In this paper, we present a particle filter based object tracking method, where a set of contextual points is used to support particle...
Multi-touch tracking algorithm requires maintaining separate identities for multi-touch points, however, it fails when independent particle filter for each object is kidnapped by neighboring targets. This is called the hijacking problem. The motion model using Markov random field (MRF) has been proposed for avoiding this problem by lowering the weight of particles which are close to neighboring touch...
In this paper, we describe a new approach to improve the video based object tracking system with particle filter using shape similarity. It deals with single object tracking whose dynamics age highly non-linear. The shape similarity between a template and estimated regions in the video sequences can be measured by their normalized cross-correlation of distance transformation. Here within this present...
In this paper, we depict a new approach for moving object tracking with particle filter by shape information. The shape similarity between a template and estimated regions in the video scene is measured by their normalized cross-correlation of distance transformed image. Our observation system of particle filter is based on shape from distance transformed edge features. Template is created instantly...
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...
Robust and real-time object tracking of any objects is a challenging task. Particle filtering has been proven very successful for non-gaussian and non-linear estimation problems. This paper describes a new approach to improve the moving object tracking system with particle filter using shape similarity. The shape similarity between a template and estimated regions in the video sequences can be measured...
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