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Almost all multi-target tracking systems have to generate point estimates for the targets, e.g., for displaying the tracks. The novel idea in this paper is to consider point estimates for multi-target states that are optimal according to a kernel distance measure. Because the kernel distance is a metric on point sets and ignores the target labels, shortcomings of Minimum Mean Squared Error (MMSE)...
Visual object tracking in video can be formulated as a time varying appearance-based binary classification problem. Tracking algorithms need to adapt to changes in both foreground object appearance as well as varying scene backgrounds. Fusing information from multimodal features (views or representations) typically enhances classification performance without increasing classifier complexity when image...
This paper describes a method of tracking multiple persons with occlusions using stereo. We previously developed an accurate and stable tracking method using overlapping silhouette templates which considers how persons overlap in the image. It realized a fast tracking by using an approximated likelihood map based on kernel density estimation. The method, however, treated only two overlapping persons...
This paper considers the problem of bearings only tracking of manoeuvring targets. A learning particle filtering algorithm is proposed which can estimate both the unknown target states and unknown model parameters. The algorithm performance is validated and tested over a challenging scenario with abrupt manoeuvres. A comparison of the proposed algorithm with the Interacting Multiple Model (IMM) filter...
This paper describes a method of tracking multiple persons with occlusions using stereo. Many previous stereo-based systems track each person separately and do not explicitly handle such occlusions. We previously developed an accurate, stable tracking method using overlapping silhouette templates which considers how persons overlap in the image. However, because the method uses a particle filter,...
A major problem in the application of particle filters to multiple target tracking is the loss of modes due to sample degeneracy. A particle filter based on the notion of uniform sampling is developed to address this issue. The proposed particle filter approximates uniform sampling in an auxiliary variable framework. Simulation results show that the proposed method provides significantly improved...
A novel control scheme for binocular robotic visual tracking problem is proposed. First we obtain the parallel configuration using rotation matrix and translational vector which can be both derived by corresponding points. Second, the binocular vision system provide the position in space and the references are used to estimate the transformation matrix to predict the image motion of the virtually...
Abstract-In this paper, a new method is proposed for hand tracking based on a density approximation and optimization method. Considering tracking as a classification problem, we train an approximator to recognize hands from its background. This procedure is done by extracting feature vector of every pixel in the first frame and then building an approximator to construct a virtual optimized surface...
In this paper, a data-driven extension of the variational algorithm is proposed. Based on a few selected sensors, target tracking is performed distributively without any information about the observation model. Tracking under such conditions is possible if one exploits the information collected from extra inter-sensor RSSI measurements. The target tracking problem is formulated as a kernel matrix...
This paper presents algorithms for consistent joint localisation and tracking of multiple targets in wireless sensor networks under the decentralised data fusion (DDF) paradigm where particle representations of the state posteriors are communicated. This work differs from previous work as more generalised methods have been developed to account for correlated estimation errors that arise due to common...
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