12,462 research outputs found

    Big Data Mining to Construct Truck Tours

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    Cross-Border shipping of goods among different distributors is an essential part of transportation across Canada and U.S. These two countries are heavily dependent on border crossing locations to facilitate international trade between each other. This research considers the identification of the international tours accomplishing the shipping of goods. A truck tour is a round trip where a truck starts its journey from its firm or an industry, performing stops for different purposes that include taking a rest, fuel refilling, and transferring goods to multiple locations, and returns back to its initial firm location. In this thesis, we present a three step method on mining GPS truck data to identify all possible truck tours belonging to different carriers. In the first step, a clustering technique is applied on the stop locations to discover the firm for each carrier. A modified DBSCAN algorithm is proposed to achieve this task by automatically determining the two input parameters based on the data points provided. Various statistical measures like count of unique trucks and count of truck visits are applied on the resulting clusters to identify the firms of the respective carriers. In the second step, we tackle the problem of classifying the stop locations into two types: primary stops, where goods are transferred, and secondary stops like rest stations, where vehicle and driver needs are met. This problem is solved using one of the trade indicator called Specialization Index. Moreover, several set of features are explored to build the classification model to classify the type of stop locations. In the third step, having identified the firm, primary and secondary locations, an automated path finder is developed to identify the truck tours starting from each firm. The results of the specialization index and the feature-based classification in identifying stop events are compared with the entropy index from previous work. Experimental results show that the proposed set of cluster features significantly add classification power to our model giving 98.79% accuracy which in turn helps in discovering accurate tours

    Towards Intelligent Crowd Behavior Understanding through the STFD Descriptor Exploration

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    Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of input video signals. This integrated solution defines an image descriptor (named spatio-temporal feature descriptor - STFD) that reflects the global motion information of crowds over time. A CNN has then been adopted to classify dominant or large-scale crowd abnormal behaviors. The work reported has focused on: 1) detecting moving objects in online (or near real-time) manner through spatio-temporal segmentations of crowds that is defined by the similarity of group trajectory structures in temporal space and the foreground blocks based on Gaussian Mixture Model (GMM) in spatial space; 2) dividing multiple clustered groups based on the spectral clustering method by considering image pixels from spatio-temporal segmentation regions as dynamic particles; 3) generating the STFD descriptor instances by calculating the attributes (i.e., collectiveness, stability, conflict and crowd density) of particles in the corresponding groups; 4) inputting generated STFD descriptor instances into the devised convolutional neural network (CNN) to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the PETS database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements of this novel approach in terms of accuracy and efficiency for detecting crowd anomalies

    Towards Intelligent Crowd Behavior Understanding through the STFD Descriptor Exploration

    Get PDF
    Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of input video signals. This integrated solution defines an image descriptor (named spatio-temporal feature descriptor - STFD) that reflects the global motion information of crowds over time. A CNN has then been adopted to classify dominant or large-scale crowd abnormal behaviors. The work reported has focused on: 1) detecting moving objects in online (or near real-time) manner through spatio-temporal segmentations of crowds that is defined by the similarity of group trajectory structures in temporal space and the foreground blocks based on Gaussian Mixture Model (GMM) in spatial space; 2) dividing multiple clustered groups based on the spectral clustering method by considering image pixels from spatio-temporal segmentation regions as dynamic particles; 3) generating the STFD descriptor instances by calculating the attributes (i.e., collectiveness, stability, conflict and crowd density) of particles in the corresponding groups; 4) inputting generated STFD descriptor instances into the devised convolutional neural network (CNN) to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the PETS database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements of this novel approach in terms of accuracy and efficiency for detecting crowd anomalies

    Directional Dense-Trajectory-based Patterns for Dynamic Texture Recognition

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    International audienceRepresentation of dynamic textures (DTs), well-known as a sequence of moving textures, is a challenging problem in video analysis due to disorientation of motion features. Analyzing DTs to make them "under-standable" plays an important role in different applications of computer vision. In this paper, an efficient approach for DT description is proposed by addressing the following novel concepts. First, beneficial properties of dense trajectories are exploited for the first time to efficiently describe DTs instead of the whole video. Second, two substantial extensions of Local Vector Pattern operator are introduced to form a completed model which is based on complemented components to enhance its performance in encoding directional features of motion points in a trajectory. Finally, we present a new framework, called Directional Dense Trajectory Patterns , which takes advantage of directional beams of dense trajectories along with spatio-temporal features of their motion points in order to construct dense-trajectory-based descriptors with more robustness. Evaluations of DT recognition on different benchmark datasets (i.e., UCLA, DynTex, and DynTex++) have verified the interest of our proposal

    Characterization of complex networks: A survey of measurements

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    Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the proper application and interpretation of measurements.Comment: A working manuscript with 78 pages, 32 figures. Suggestions of measurements for inclusion are welcomed by the author

    Artificial Ontogenies: A Computational Model of the Control and Evolution of Development

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    Understanding the behaviour of biological systems is a challenging task. Gene regulation, development and evolution are each a product of nonlinear interactions between many individual agents: genes, cells or organisms. Moreover, these three processes are not isolated, but interact with one another in an important fashion. The development of an organism involves complex patterns of dynamic behaviour at the genetic level. The gene networks that produce this behaviour are subject to mutations that can alter the course of development, resulting in the production of novel morphologies. Evolution occurs when these novel morphologies are favoured by natural selection and survive to pass on their genes to future generations. Computational models can assist us to understand biological systems by providing a framework within which their behaviour can be explored. Many natural processes, including gene regulation and development, have a computational element to their control. Constructing formal models of these systems enables their behaviour to be simulated, observed and quantified on a scale not otherwise feasible. This thesis uses a computational simulation methodology to explore the relationship between development and evolution. An important question in evolutionary biology is how to explain the direction of evolution. Conventional explanations of evolutionary history have focused on the role of natural selection in orienting evolution. More recently, it has been argued that the nature of development, and the way it changes in response to mutation, may also be a significant factor. A network-lineage model of artificial ontogenies is described that incorporates a developmental mapping between the dynamics of a gene network and a cell lineage representation of a phenotype. Three series of simulation studies are reported, exploring: (a) the relationship between the structure of a gene network and its dynamic behaviour; (b) the characteristic distributions of ontogenies and phenotypes generated by the dynamics of gene networks; (c) the effect of these characteristic distributions on the evolution of ontogeny. The results of these studies indicate that the model networks are capable of generating a diverse range of stable behaviours, and possess a small yet significant sensitivity to perturbation. In the context of developmental control, the intrinsic dynamics of the model networks predispose the production of ontogenies with a modular, quasi-systematic structure. This predisposition is reflected in the structure of variation available for selection in an adaptive search process, resulting in the evolution of ontogenies biased towards simplicity. These results suggest a possible explanation for the levels of ontogenetic complexity observed in biological organisms: that they may be a product of the network architecture of developmental control. By quantifying complexity, variation and bias, the network-lineage model described in this thesis provides a computational method for investigating the effects of development on the direction of evolution. In doing so, it establishes a viable framework for simulating computational aspects of complex biological systems
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