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In this article, we present an algorithm to track objects in complex environments like, large variations in scale and orientation, background clutters, illumination changes, pose variation and occlusion. A multilayer perceptron based discriminative appearance model is constructed to distinguish the objects from their cluttered backgrounds. Moments of the binary image are used to estimate scale and...
Haptic guidance has previously been employed to improve human performance in control tasks. This paper presents an experiment to evaluate whether haptic feedback can be used to help humans learn a compensatory tracking task. In the experiment, participants were divided into two groups: the haptic group and the no-aid group. The haptic group performed a first training phase with haptic feedback and...
Adaptive, model-free control of Type 1 Diabetes Mellitus (T1DM) is a lack in the field of diabetes control, since, most of the applied control strategies are model-based ones. The main problem is that difficult to formulate exact mathematical models to replicate the physiological processes, not just because of their behavior, rather then these processes are changing patient-by-patient. Furthermore,...
Occlusion is one important problem in single object tracking. However, conventional methods are not capable of making full use of the spatial information because of occlusion, which may lead to the drift. In this paper, we propose a robust patches-based tracking method via sparse representation, namely RPSR, which selects the unoccluded patches, and adaptively assigns larger contribution factors to...
Existing tracking approaches are design-varied. Several features have been proposed to describe low, mid and high level cues and used to drive frame to frame tracking correspondences under mainly two strategies: purely matching and discriminative matching. However, despite the enormous amount of approaches proposed, single object tracking is still an unresolved task. In this paper we explore a, to...
Visual multi-object tracking is an important task within the field of computer vision. The goal of this paper is to track a variable number of unknown objects in complex scenes automatically using a moving and un-calibrated camera and it devotes to overcome the challenging problems including illumination and scale variations, viewpoint variations and significant occlusions, etc. In this paper, a binary...
In this paper, we propose a boosting based tracking framework using transfer learning. To deal with complex appearance variations, the proposed tracking framework tries to utilize discriminative information from previous frames to conduct the tracking task in the current frame, and thus transfers some prior knowledge from the previous source data domain to the current target data domain, resulting...
In this paper, a robust visual tracking method is proposed by using Least Absolute Shrinkage and Selection Operator (Lasso) in a particle filter framework. First, to locate the tracking target at a new frame, each target candidate is sparsely represented in the space spanned by sampling particle images and sparsity vector. The lasso can replace the sparse approximation problem by a convex problem...
A method is proposed for tracking objects in face with varying viewpoints and partial occlusions. A low-dimensional subspace is built to model the appearance of the target. And each image sample is presented as a coefficient vector in the subspace. A collection of image patches are sampled as the candidates of the object image region in the current frame, and their likelihoods of being the object...
A problem arising at multiple target tracking with particle filters typically in vision has been claimed and a likelihood adjustment method has been proposed. First, classify tracking methods by particle filters into two categories, detection first tracking and particle dependent tracking. Then this research focus on the particle dependent tracking. It involves the problem in case of multiple target...
Primates demonstrate an outstanding ability of gaining a continuous tracking of target in cluttered environments after target disappears in the scene for a long time while it is still a challenge for artificial visual system to do so. Research in psychology indicates that selective visual attention with two attention selection processes is crucial to visual tracking. This paper presents a novel visual...
In this paper we present an application of computer vision techniques to obtain specific information about the behaviour of the people passing in front of a target scene. This is done by analyzing videos captured by cameras monitoring an area under surveillance. The target scene can be a large plasma screen, a projected image, an advertising poster or a shop window. An example of the type of information...
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