作者：
Zhang, ZhaoJiao, XiaohongYanshan Univ
Engn Res Ctr Minist Educ Intelligent Control Syst & Intelligen Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ
Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China
Short-term traffic flow prediction is of great significance in intelligent transportation. In recent years, with the development of information collection technology and deep learning algorithms, neural network models...
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Short-term traffic flow prediction is of great significance in intelligent transportation. In recent years, with the development of information collection technology and deep learning algorithms, neural network models have become increasingly popular in traffic flow prediction research. However, accurate and fast prediction is a challenge because of the uncertain feature of traffic flow and limitations of the model structure. Motivated by this issue, this paper uses a dual-branch grammar model to extract the deep spatio-temporal features of historical traffic information. Each branch combines the grammar structure with the gated convolution operation to realize the interaction between the implicit features of different traffic parameters. Moreover, scaled exponential linear units (Selu) are used as an activation function for gated convolution operation to enhance the convergence effect of network training. And then, a wide attention module is designed to weigh the extracted deep spatio-temporal features to increase the model's prediction accuracy with a slight increase in computational cost. Finally, actual traffic data from Caltrans Performance Measurement System (PeMS) is used to evaluate the prediction performance with the result that the proposed prediction method outperforms other methods in terms of prediction accuracy. In addition, this paper proves the Selu function's importance by analysing the training error's convergence effect and explains the role of wide attention in the prediction task through visualization and statistical analysis operations.
This paper starts with the network level in the realization of industrial control protocol, and gives a fuzzy security test method based on the grammatical model. This paper first expounds the concept of protocol desc...
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ISBN:
(纸本)9781728116648
This paper starts with the network level in the realization of industrial control protocol, and gives a fuzzy security test method based on the grammatical model. This paper first expounds the concept of protocol description model, then gives the definition of related grammar, and proposes a grammar model for industrial control protocol based on high-order attribute grammar. The model can accurately describe the format and constraint relationship of the structured data of the industrial control protocol. On this basis, the model proposes a fuzzy security test algorithm, combined with the characteristics of the industrial control protocol, elaborates on the analysis tree structure, test case generation and mutation strategy. At the same time, the model performs comparative experiments by simulating Modbus/TCP communication between the master and slave stations in the industrial control system (based on Modbus/TCP and IEC-104 protocols), and statistically tests the test results. The above method verifies that anomalous results can still be found at a lower time cost when generating fewer test cases. To a certain extent, the experimental results reflect the improved relevance and effectiveness of test cases.
To handle the pedestrian appearance and pose variations in complex traffic environments, we present one part-based pedestrian detection approach using a stochastic grammar model in this paper. The And-Or graph model i...
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ISBN:
(纸本)9781479903801
To handle the pedestrian appearance and pose variations in complex traffic environments, we present one part-based pedestrian detection approach using a stochastic grammar model in this paper. The And-Or graph model is introduced to represent the human body as an assembly of compositional and reconfigurable parts. Thus, the task of detection is converted into the human parsing problem, which is a Bayesian inference process. We model the appearance of pedestrian parts in a rich feature representation. This appearance model enhances the Histogram of Gradients (HoG) map with Active Basis model (ABM), which is a sparse deformable template depicting salient structures of objects. Then, geometry constraints among parts are described by Gaussian distributions. Finally, the bottom-up parsing inference is conducted by aggregating scores to get the pedestrian detection responses. In experiments, we show the superiority of our appearance model, as well as the reliable pedestrian detection results of our approach in complex traffic scenes.
Traffic surveillance is an important topic in intelligent transportation systems (ITS). Robust vehicle detection is one challenging problem for complex traffic surveillance. In this paper, we propose an efficient vehi...
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Traffic surveillance is an important topic in intelligent transportation systems (ITS). Robust vehicle detection is one challenging problem for complex traffic surveillance. In this paper, we propose an efficient vehicle detection method by designing vehicle detection grammars and handling partial occlusion. The grammar model is implemented by novel detection grammars, including structure, deformation and pairwise SVM grammars. First, the vehicle is divided into its constitute parts, called semantic parts, which can represent the vehicle effectively. To increase the robustness of part detection, the semantic parts are represented by their detection score maps. The semantic parts are further divided into sub-parts automatically. The two-layer division of the vehicle is modeled into a grammar model. Then, the grammar model is trained by a designed training procedure to get ideal grammar parameters, including appearance models and grammar productions. After that, vehicle detection is executed by a designed detection procedure with respect to the grammar model. Finally, the issue of vehicle occlusion is handled by designing and training specific grammars. The strategy adopted by our method is first to divide the vehicle into the semantic parts and sub-parts, then to train the grammar productions for semantic parts and sub-parts by introducing novel pairwise SVM grammars and finally to detect the vehicle by applying the trained grammars. Experiments in practical urban scenarios are carried out for complex traffic surveillance. It can be shown that our method adapts to partial occlusion and various challenging cases. (C) 2015 Elsevier Ltd. All rights reserved.
In this paper, we investigate a novel reconfigurable part-based model, namely And-Or graph model, to recognize object shapes in images. Our proposed model consists of four layers: leaf-nodes at the bottom are local cl...
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In this paper, we investigate a novel reconfigurable part-based model, namely And-Or graph model, to recognize object shapes in images. Our proposed model consists of four layers: leaf-nodes at the bottom are local classifiers for detecting contour fragments;or-nodes above the leaf-nodes function as the switches to activate their child leaf-nodes, making the model reconfigurable during inference;and-nodes in a higher layer capture holistic shape deformations;one root-node on the top, which is also an or-node, activates one of its child and-nodes to deal with large global variations (e.g. different poses and views). We propose a novel structural optimization algorithm to discriminatively train the And-Or model from weakly annotated data. This algorithm iteratively determines the model structures (e.g. the nodes and their layouts) along with the parameter learning. On several challenging datasets, our model demonstrates the effectiveness to perform robust shape-based object detection against background clutter and outperforms the other state-of-the-art approaches. We also release a new shape database with annotations, which includes more than 1500 challenging shape instances, for recognition and detection.
This paper presents a hierarchical-compositional model of human faces, as a three-layer AND-OR graph to account for the structural variabilities over multiple resolutions. In the AND-OR graph, an AND-node represents a...
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This paper presents a hierarchical-compositional model of human faces, as a three-layer AND-OR graph to account for the structural variabilities over multiple resolutions. In the AND-OR graph, an AND-node represents a decomposition of certain graphical structure, which expands to a set of OR-nodes with associated relations;an OR-node serves as a switch variable pointing to alternative AND-nodes. Faces are then represented hierarchically: The first layer treats each face as a whole, the second layer refines the local facial parts jointly as a set of individual templates, and the third layer further divides the face into 15 zones and models detail facial features such as eye corners, marks, or wrinkles. Transitions between the layers are realized by measuring the minimum description length (MDL) given the complexity of an input face image. Diverse face representations are formed by drawing from dictionaries of global faces, parts, and skin detail features. A sketch captures the most informative part of a face in a much more concise and potentially robust representation. However, generating good facial sketches is extremely challenging because of the rich facial details and large structural variations, especially in the high-resolution images. The representing power of our generative model is demonstrated by reconstructing high-resolution face images and generating the cartoon facial sketches. Our model is useful for a wide variety of applications, including recognition, nonphotorealisitc rendering, superresolution, and low-bit rate face coding.
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