Ensembles of multiple (active) cameras yield an important ingredient in modern tracking and surveillance applications. They overcome the limited fields-of-view of single cameras, however, require robust procedures for...
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ISBN:
(纸本)9781424421749
Ensembles of multiple (active) cameras yield an important ingredient in modern tracking and surveillance applications. They overcome the limited fields-of-view of single cameras, however, require robust procedures for handing over tracking tasks from one camera to another. In this paper a calibration-free procedure is proposed that allows for fast and reliable camera hand-over in Ambient Intelligence (AmI) applications. The approach is based on online acquisition of scenario-specific target models and especially solves the problem of significant changes in object view during hand-over. Real-world results acquired in an AmI environment prove the effectiveness of our technique.
Spatial-temporal local motion features have shown promising results in complex human action classification. Most of the previous works [6],[16],[21] treat these spatial- temporal features as a bag of video words, omit...
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Spatial-temporal local motion features have shown promising results in complex human action classification. Most of the previous works [6],[16],[21] treat these spatial- temporal features as a bag of video words, omitting any long range, global information in either the spatial or temporal domain. Other ways of learning temporal signature of motion tend to impose a fixed trajectory of the features or parts of human body returned by tracking algorithms. This leaves little flexibility for the algorithm to learn the optimal temporal pattern describing these motions. In this paper, we propose the usage of spatial-temporal correlograms to encode flexible long range temporal information into the spatial-temporal motion features. This results into a much richer description of human actions. We then apply an unsupervised generative model to learn different classes of human actions from these ST-correlograms. KTH dataset, one of the most challenging and popular human action dataset, is used for experimental evaluation. Our algorithm achieves the highest classification accuracy reported for this dataset under an unsupervised learning scheme.
This paper addresses the problem of stabilizing a fully-actuated rigid body. The problem is formulated considering the natural space for rigid body configurations, the Special Euclidean group SE(3). The proposed solut...
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ISBN:
(纸本)9781424431236
This paper addresses the problem of stabilizing a fully-actuated rigid body. The problem is formulated considering the natural space for rigid body configurations, the Special Euclidean group SE(3). The proposed solution consists of an output-feedback controller for force and torque actuation that guarantees almost global asymptotic stability of the desired equilibrium point. As such the equilibrium point is a stable attractor for all initial conditions except for those in a nowhere dense set of measure zero. As an additional feature, the controller is required to verify prescribed bounds on the actuation.
A classification technique using the neural networks has recently been developed. We apply a neural network of learning vector quantization (LVQ) to classify remote-sensing data, including microwave and optical sensor...
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We introduce a neural network of self-organizing feature map (SOM) to classify remote-sensing data, including microwave and optical sensors, for the estimation of areas of planted rice. This method is an unsupervised ...
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We model the Sum and Product Riddle inpublic announcement logic, which is interpreted on an epistemic Kripke model. The model is symbolically represented as a finite state program with n agents. A model checking metho...
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In the this paper, a CMAC-Q-learning based Dyna agent is presented to relieve the problem of learning speed in reinforcement learning, in order to achieve the goals of shortening training process and increasing the le...
In the this paper, a CMAC-Q-learning based Dyna agent is presented to relieve the problem of learning speed in reinforcement learning, in order to achieve the goals of shortening training process and increasing the learning, speed. We combine CMAC, Q-learning, and prioritized sweeping techniques to construct the Dyna agent in which a Q-learning is trained for policy learning; meanwhile, model approximators, called CMAC-model and CMAC-R-model, are in charge of approximating the environment model. The approximated model provides the Q-learning with virtual interaction experience to further update the policy within the time gap when there is no interplay between the agent and the real environment. The Dyna agent switches seamlessly between the real environment and the virtual environment model for the objective of policy learning. A simulation for controlling a differential-drive mobile robot has been conducted to demonstrate that the proposed method can preliminarily achieve the design goal.
This paper deals with the application of a method developed for the compensation of harmonic currents generated by nonlinear loads, connected to the utility grid, using three phase dc/ac converters. The converters are...
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Camera handoff is a crucial step to generate a continuously tracked and consistently labeled trajectory of the object of interest in multi-camera surveillance systems. Most existing camera handoff algorithms concentra...
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Camera handoff is a crucial step to generate a continuously tracked and consistently labeled trajectory of the object of interest in multi-camera surveillance systems. Most existing camera handoff algorithms concentrate on data association, namely consistent labeling, where images of the same object are matched across different cameras. However, most real-time object tracking systems see a decrease in the system's frame rate as the number of tracked objects increases. To address this issue, we propose to incorporate an adaptive resource management mechanism into camera handoff. In so doing, cameras¿ resources can be dynamically allocated to multiple objects according to their priorities and hence the required minimum frame rate can be maintained. Experimental results illustrate that the proposed camera handoff algorithm is capable of maintaining a constant frame rate and of achieving a substantially improved handoff success rate by approximately 20% in comparison with the algorithm presented by Khan and Shah.
Recently, particle filters have been applying to many robotic problems including the simultaneous localization and mapping (SLAM). Specifically, SLAM approaches employing Rao-Blackwellized particle filter (RBPF) have ...
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Recently, particle filters have been applying to many robotic problems including the simultaneous localization and mapping (SLAM). Specifically, SLAM approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, no research is conducted to analyze representation of the results of particle filtering. After finishing the particle filtering, the results such as a map and a path are stored in the separate particles. In most cases, the result of the particle that has the highest importance weight is represented as the result. However, this approach does not give the best result all the time. Thus, We provide the analysis of final representation of particle filtering. In this paper, we compares several methods to derive the final representation of the result after finishing RBPF-SLAM. According to the result, combining data of each particle provides the better result with high probability than using just data of a particle such as the highest weighted particle representation.
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