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With the advance of mobile electronic devices and the development of positioning technology, a large volume of spatio-temopral data are collected in the form of desultorily data streams, which contain a lot of potential information. In this study, we focus on discovering the composition relationships between observation moving objects in a long period. Such research can be widely used in military...
This paper extends our previous work on deriving meaningful storm patterns from very large rainfall data. In an earlier work, we described MapReduce-based algorithms to identify three types of the storms: local, hourly and overall storms. In general, local storms have temporal characteristics of the storms at a particular site, hourly storms have spatial characteristics of the storms at a particular...
In the paper, an efficient spatial clustering algorithm, the improved DBSCAN (Density Based Spatial Clustering of Applications with Noise), is proposed, for cluster analysis of trajectory points gathered from Digital Movies Mobile Playing Systems(DMSs). By searching the density of points which are greater than a given threshold in rural areas, the density-reachable maximum movie playing clusters are...
This work proposes a trajectory clustering-based approach for segmenting flow patterns in high density crowd videos. The goal is to produce a pixel-wise segmentation of a video sequence (static camera), where each segment corresponds to a different motion pattern. Unlike previous studies that use only motion vectors, we extract full trajectories so as to capture the complete temporal evolution of...
For trajectory model to study mining, using Vector Fields on Manifold instead of the Euclidean distance to metric similarity between trajectories, multi scale transform method is used to optimize the mapping in the Vector Fields on Manifold trajectory distance calculation and use Som algorithm for training a classification model. This method will be the trajectory shape features to measure the similarity...
The technical advances of positioning technologies enable us to track animal movements at finer spatial and temporal scales, and further help to discover a variety of complex interactive relationships. In this paper, considering the loose gathering characteristics of the real-life groups' members during the movements, we propose two kinds of loose group movement patterns and corresponding discovery...
In recent years, special interest has been paid to the solution of sector design problem. The airspace is partitioned into sectors, each of them being controlled by a group of controllers. Airspace sectors should be designed cautiously, ensuring that no sector would be overloaded during the day. The objective of an airspace design process is to adapt the airspace according to the evolution of the...
Spatiotemporal clustering is a process of grouping a set of objects based on their spatial and temporal similarities. In this paper we propose two new spatiotemporal clustering algorithms, called Spatiotemporal Shared Nearest Neighbor clustering algorithm (ST-SNN), and Spatiotemporal Separated Shared Nearest Neighbor clustering algorithm (ST-SEP-SNN), to cluster overlapping polygons that can change...
The opportunities to understand human-mobility have increased significantly of late with the rapid adoption of wireless devices that report locations frequently. In this work1, we utilize one such rich data-set comprising of nationwide call data records from several million users to analyze and understand their location patterns. We define a location pattern as the set of locations visited by a user,...
This work aims to identify abnormal behaviors from the analysis of humans or vehicles' trajectories. A set of normal trajectories' prototypes is extracted by means of a novel unsupervised learning technique: the scene is adaptively partitioned into zones by using the distribution of the training set and each trajectory is represented as a sequence of symbols by taking into account positional information...
Given a set of moving object trajectories, it is of interest to find a group of objects, called a convoy, that are spatially density-connected for a certain duration of time. However, existing convoy discovery algorithms have a critical problem of accuracy; they tend to both miss larger convoys and retrieve invalid ones where the density-connectivity among the objects is not completely satisfied....
In this paper, we formulate the feature clustering problem for vehicle detection and tracking as a general MAP problem and solve it using MCMC. The proposed approach exhibits two advantages over existing methods: general Bayesian model can handle arbitrary objective functions and MCMC guarantees global optimal solution. Our algorithm is validated on real-world traffic video sequences, and is shown...
In this paper, a novel 3-D motion trajectory signature is introduced to serve as an effective description to the raw trajectory. More importantly, based on the trajectory signature, a probabilistic model-based cluster signature is further developed for modeling a motion class. The cluster signature is a mixture model-based motion description that is useful for motion class perception, recognition...
Recently a large amount of research has been devoted to automatic activity analysis. Typically, activities have been defined by their motion characteristics and represented by trajectories. These trajectories are collected and clustered to determine typical behaviors. This paper evaluates different similarity measures and clustering methodologies to catalog their strengths and weaknesses when utilized...
We propose an object detection system that uses the locations of tracked low-level feature points as input, and produces a set of independent coherent motion regions as output. As an object moves, tracked feature points on it span a coherent 3D region in the space-time volume defined by the video. In the case of multi-object motion, many possible coherent motion regions can be constructed around the...
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