This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image...
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This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image, assuming moving vehicles may cause pixel intensities and local texture to change, and then by identifying such pixel changes to detect vehicles. In this research, multiple pattern classifiers including LDA + Adaboost, SVM, and Random Forests are used to detect vehicles that are passing through virtual loops. We extract fourteen pattern features (related to foreground area, texture change, and luminance and contrast in the local virtual loop zone and the global image) to train pattern classifiers and then detect vehicles. As experimental results illustrate, the proposed approach is quite robust to detect vehicles under complex dynamic environments, and thus is able to improve the accuracy of traffic data collection in all weather for long term.
With the advantage of simulating the details of a transportation system, the “microsimulation” of a traffic system has long been a hot topic in the Intelligent Transportation systems (ITS) research. The Cellular Aut...
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With the advantage of simulating the details of a transportation system, the “microsimulation” of a traffic system has long been a hot topic in the Intelligent Transportation systems (ITS) research. The Cellular Automata (CA) and the Multi-Agent System (MAS) modeling are two typical methods for the traffic microsimulation. However, the computing burden for the microsimulation and the optimization based on it is usually very heavy. In recent years the Graphics Processing Units (GPUs) have been applied successfully in many areas for parallel computing. Compared with the traditional CPU cluster, GPU has an obvious advantage of low cost of hardware and electricity consumption. In this paper we build an MAS model for a road network of four signalized intersections and we use a Genetic Algorithm (GA) to optimize the traffic signal timing with the objective of maximizing the number of the vehicles leaving the network in a given period of time. Both the simulation and the optimization are accelerated by GPU and a speedup by a factor of 195 is obtained. In the future we will extend the work to large scale road networks.
Linear wireless sensor networks are characterized by a linear topology and multihop forwarding patterns. Sensors closer to the base station are usually required to forward a large amount of traffic for sensors far...
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ISBN:
(纸本)9781457715860
Linear wireless sensor networks are characterized by a linear topology and multihop forwarding patterns. Sensors closer to the base station are usually required to forward a large amount of traffic for sensors farther from the base station, leading to the environment cannot be effectively monitored. The energy consumption balance is achieved in the paper by adjusting the distance between nodes. The distance of nodes near the base station become short because they have to forward the more traffic. The node deployment is converted to optimization problems of distance distribution between nodes. The network lifetime under different number of forwarding region is discussed. Simulation results show that this deployment strategy obtains the energy consumption balance.
Most researches of traffic incident auto-detection are based on the data from fixed detectors, which are limited by costs and position. In order to resolve this problem, existing algorithms of traffic incident automat...
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Most researches of traffic incident auto-detection are based on the data from fixed detectors, which are limited by costs and position. In order to resolve this problem, existing algorithms of traffic incident automatic detection are analyzed and compared, and an algorithm of traffic incident auto-detection are provided based on mobile-detection technology. The traffic data are grouped in 5-min intervals, analyzed by a three-layer BP neural network, and utilized for traffic incident detection. 16 traffic incidents of different locations and different levels are modeled in the simulation experiment based on VISSIM, and detection rate, false alarm rate and average detection time are adopted as indicators to evaluate the algorithm. Finally, the algorithm is proved to be effective and applicable in practice.
A qualitative control method using reinforcement learning (RL) and grey system is developed for mobile robot navigation in an unknown environment. New representation and computation mechanisms are key approaches for l...
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This book constitutes the refereed proceedings of the International Conference for Smart Health, ICSH 2014, held in Beijing, China, in July 2014. The 21 papers presented together with 4 extended abstracts were careful...
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ISBN:
(数字)9783319084169
ISBN:
(纸本)9783319084152
This book constitutes the refereed proceedings of the International Conference for Smart Health, ICSH 2014, held in Beijing, China, in July 2014. The 21 papers presented together with 4 extended abstracts were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on information sharing, integrating and extraction; health data analysis and management; clinical and medical data mining; and clinical practice and medical monitoring.
This book constitutes the refereed proceedings of the 6th International Conference on Brain Inspired Cognitive systems, BICS 2013, held in Beijing, China in June 2013. The 45 high-quality papers presented were careful...
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ISBN:
(数字)9783642387869
ISBN:
(纸本)9783642387852
This book constitutes the refereed proceedings of the 6th International Conference on Brain Inspired Cognitive systems, BICS 2013, held in Beijing, China in June 2013. The 45 high-quality papers presented were carefully reviewed and selected from 68 submissions. BICS 2013 aims to provide a high-level international forum for scientists, engineers, and educators to present the state of the art of brain inspired cognitive systems research and applications in diverse fields.
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