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We present a robust object tracking algorithm which integrates Modified Continuous Adaptive Mean shift and Particle Filtering providing a framework for state estimation in nonlinear and non-Gaussian dynamic system. In order to overcome the various kinds of clutter and distracters problem, we employ a parameter associated with the similarity measurement to update window width adaptively via calculating...
Hand tracking is an active research topic in Human Computer Interaction (HCI). In this paper, we present an improved Unscented Particle Filter (UPF) combined with the incremental Principle Component Analysis (IPCA) method for the visual hand tracking. The Singular Value Decomposition (SVD) approach is introduced to compute the sigma points and then to obtain the proposal distribution within the Unscented...
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...
The receding horizon estimation is applied to design robust visual trackers. Most recent data within the fixed size of windows is receding, and is processed to obtain an estimate of the object state at the current time. In visual tracking such a scheme improves filter accuracy by avoiding accumulated approximation errors. A newly derived unscented Kalman filter (UKF) based on the receding horizon...
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...
Target recognition and tracking is a tough task in computer vision. How to recognize a target accurately and tracking robustly is still a core problem studied by researchers. And the task is even more challenging in outdoor environment. In this paper, a recognition and tracking algorithm suitable for a natural target in outdoor environment is introduced. The natural target we choose is the overhead...
Object tracking is one of the most important tasks in computer vision. The unscented particle filter algorithm has been extensively used to tackle this problem and achieved a great success, because it uses the UKF (unscented Kalman filter) to generate a sophisticated proposal distributions which incorporates the newest observations into the state transition distribution and thus overcomes the sample...
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...
Appearance descriptor is a fundamental component for visual tracking. But the targetpsilas appearance always changes over time in video stream due to variations in illumination, pose, scale and so on. In this paper, we combine particle filter with incremental covariance descriptor update for robust visual tracking. We employ a weight factor update mechanism to account for the contributions of previous...
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