Statistical background subtraction has proved to be a robust and effective approach for segmenting and extracting objects without any prior information of the foreground objects. This paper presents two contributions ...
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Statistical background subtraction has proved to be a robust and effective approach for segmenting and extracting objects without any prior information of the foreground objects. This paper presents two contributions on this topic. The first contribution of this paper proposes a novel approach which introduces the motion mask into the Gaussian Mixture Models to reduce the errors of classical GMMs, which always classifies the moving objects as background incorrectly, and affects the accuracy of the steps followed by, when the objects are still in long periods. The second contribution regards the connected component labeling based on the contour tracking algorithm. Experimental results validate the effectiveness of the proposed approach.
Path restoring is a path searching problem in the time-dependent road network with the time constraints of origin and destination. This paper proposes a path restoring algorithm to find the possible path that vehicles...
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ISBN:
(纸本)9781467395922
Path restoring is a path searching problem in the time-dependent road network with the time constraints of origin and destination. This paper proposes a path restoring algorithm to find the possible path that vehicles may have been driving along. We mined the vehicle trajectories based on historical GPS data and then build a “popular” intersection graph based on its entropy and frequency. Then the restoring path is searched on the sub-graph of the popular intersection graph. The experiment result shows that the proposed algorithm increases 10% compared to that of using time-dependent fastest path method.
Although biometrics technology has progressed substantially, its performance is still to be improved for real applications. This paper aims to improve the accuracy of personal identification, when only single sample i...
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To minimize Rate-Distortion (RD) cost in video coding, RD Optimization (RDO) technique is adopted, which also brings increasingly computational complexity in Motion Estimation (ME), multiple reference selection and mo...
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High Efficiency Video Coding (HEVC) is the most recent video coding standard aiming to further reduce the bitrate over 50% as compared to the state-of-the-art H.264/Advanced Video Coding under the same visual quality....
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ISBN:
(纸本)9781509053179
High Efficiency Video Coding (HEVC) is the most recent video coding standard aiming to further reduce the bitrate over 50% as compared to the state-of-the-art H.264/Advanced Video Coding under the same visual quality. In order to achieve this, a number of advanced coding techniques have been adopted in HEVC, including the quadtree structure of Coding Unit (CU), Prediction Unit (PU) and Transform Unit (TU), etc. However, these coding techniques lead to a tremendous increase in HEVC encoding computations. In order to reduce the HEVC encoding computational complexity, the optimal stopping theory is employed herein to design an efficient algorithm to optimize the decision making process when choosing the best coding parameters of CU, PU and TU. Extensive comparative experimental results are performed by the proposed algorithm and another two recent works, which demonstrate that the proposed algorithm is very efficient and better in reducing the HEVC encoding computations while keeping the video quality and compression efficiency almost intact.
Learning automaton (LA) is a reinforcement learning model that aims to determine the optimal action out of a set of actions. It is characterized by updating a selection probability vector through a sequence of repetit...
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Learning automaton (LA) is a reinforcement learning model that aims to determine the optimal action out of a set of actions. It is characterized by updating a selection probability vector through a sequence of repetitive feedback cycles interacting with an environment. Decentralized learning automata (DLAs) consists of many learning automata (LAs) that learn at the same time. Each LA independently selects an action based on its own selection probability vector. In order to provide an appropriate central coordination mechanism in DLAs, this paper proposes a novel decentralized coordination learning automaton (DCLA) using a new selection probability vector which is combined with the probability vectors derived from both LA and estimation of distribution algorithm (EDA). LA contributes to the own learning experience of each LA while EDA estimates the distribution of the whole swarm's promising individuals. Thus, decentralized LAs can be coordinated by EDA using the swarm's comprehensive knowledge. The proposed automaton is applied to solve the real problem of meta-task scheduling in heterogeneous computing system. Extensive experiments demonstrate a superiority of DCLA over other counterpart algorithms. The results show that the proposed DCLA provides an effective and efficient way to coordinate LAs for solving complicated problems.
The urban expressway system model based on hybrid petri nets is proposed. With the traffic detecting cameras and coils, the strategies of traffic signal lights and warning lights are enforced with the aim to prevent t...
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ISBN:
(纸本)9781509018987
The urban expressway system model based on hybrid petri nets is proposed. With the traffic detecting cameras and coils, the strategies of traffic signal lights and warning lights are enforced with the aim to prevent the large-scale congestion in emergencies (e.g. accidents). The simulation results verify the effectiveness of the proposed model and the warning light strategy is demonstrated to be suitable for traffic accidents and congestion in the accident-prone weaving sections in the urban expressways.
The progress of location-based services has led to severe concerns about location privacy leakage. However, existing methods are still incompetent for effective and efficient location privacy preservation (LPP). They ...
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In this paper, we proposed a novel and practical solution for the real-time indoor localization of autonomous driving in parking lots. High-level landmarks, the parking slots, are extracted and enriched with labels to...
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This paper presents a 3-transistor CMOS active pixel structure with in-pixel correlated double sampling. Designed in a standard 0.18 μm CMOS process, the structure effectively suppresses temporal noise and fixed patt...
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