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In this paper, a robust visual tracking method is proposed to track an object in dynamic conditions that include motion blur, illumination changes, pose variations, and occlusions. To cope with these challenges, multiple trackers with different feature descriptors are utilized, and each of which shows different level of robustness to certain changes in an(More)
Hydrogen sulfide (H(2)S) and nitric oxide (NO) are endogenously synthesized from l-cysteine and l-arginine, respectively. They might constitute a cooperative network to regulate their effects. In this study, we investigated whether H(2)S could affect NO production in rat vascular smooth muscle cells (VSMCs) stimulated with interleukin-1beta (IL-1beta).(More)
In this paper we present a general solution for multi-target tracking with superpositional measurements. Measurements that are functions of the sum of the contributions of the targets present in the surveillance area are called superpositional measurements. We base our modelling on Labeled Random Finite Set (RFS) in order to jointly estimate the number of(More)
In this paper, we propose an efficient and accurate visual tracker equipped with a new particle filtering algorithm and robust subspace learning-based appearance model. The proposed visual tracker avoids drifting problems caused by abrupt motion changes and severe appearance variations that are well-known difficulties in visual tracking. The proposed(More)
Multi-static Doppler-shift has re-emerged recently in the target tracking literature along with passive sensing, especially for aircraft tracking. Tracking with multi-static Doppler only measurement requires efficient multi-sensor fusion approach and optimal sensor network configuration if possible. In this paper, we present a solution for multi-target(More)
In this paper, we propose a novel implementation of the probability hypothesis density (PHD) filter based on the sequential Monte Carlo (SMC) method called SMC-PHD filter. The SMC-PHD filter is analogous to the sequential importance sampling which generates samples using an importance sampling (IS) function. Even though this filter permits general class of(More)
In this correspondence, a new multi-target tracking (MTT) algorithm based on the probability hypothesis density (PHD) filtering framework is designed in order to improve tracking performance via the proposal of two contributions. First, unlike typical existing systems, Doppler information is additionally employed to enhance the clutter rejection capability.(More)