Discovery of stop regions for understanding repeat travel behaviors of moving objects

GPS trajectory dataset with high sampling-rates is usually in large volume that challenges the processing efficiency. Most of the data points on trajectories are useless. This paper summarizes trajectories using stop points. We define a new concept of stay stability (i.e., time dividing distance or reciprocal of speed) between any two GPS points to detect stop points on individual trajectories. We propose a novel Mining Repeat Travel Behaviors Using Stop Regions (MRTBUSR) method. In MRTBUSR, a stop region is a popular region containing a certain number of close stop points that can be grouped into a cluster. We then retrieve common sequences of stop regions to denote repeat route patterns and further analyze the stop durations on a stop region to find repeat travel behaviors. The experiments on 20 labeled trajectories selected from GeoLife demonstrated the semantic effect, accuracy and near linear efficiency of our proposed method.

Efficiently retrieving longest common route patterns of moving objects by summarizing turning regions

The popularity of online location services provides opportunities to discover useful knowledge from trajectories of moving objects. This paper addresses the problem of mining longest common route (LCR) patterns. As a trajectory of a moving object is generally represented by a sequence of discrete locations sampled with an interval, the different trajectory instances along the same route may be denoted by different sequences of points (location, timestamp). Thus, the most challenging task in the mining process is to abstract trajectories by the right points. We propose a novel mining algorithm for LCR patterns based on turning regions (LCRTurning), which discovers a sequence of turning regions to abstract a trajectory and then maps the problem into the traditional problem of mining longest common subsequences (LCS). Effectiveness of LCRTurning algorithm is validated by an experimental study based on various sizes of simulated moving objects datasets. © 2011 Springer-Verlag.

Inter-Frame Change Directing online clustering of multiple moving objects for video-based sensor networks

Recognition of multiple moving objects is a very important task for achieving user-cared knowledge to send to the base station in wireless video-based sensor networks. However, video based sensor nodes, which have constrained resources and produce huge amount of video streams continuously, bring a challenge to segment multiple moving objects from the video stream online. Traditional efficient clustering algorithms such as DBSCAN cannot run time-efficiently and even fail to run on limited memory space on sensor nodes, because the number of pixel points is too huge. This paper provides a novel algorithm named Inter-Frame Change Directing Online clustering (IFCDO clustering) for segmenting multiple moving objects from video stream on sensor nodes. IFCDO clustering only needs to group inter-frame different pixels, thus it reduces both space and time complexity while achieves robust clusters the same as DBSCAN. Experiment results show IFCDO clustering excels DBSCAN in terms of both time and space efficiency. © 2008 IEEE.

DoSTra: Discovering common behaviors of objects using the duration of staying on each location of trajectories

Since semantic trajectories can discover more semantic meanings of a user's interests without geographic restrictions, research on semantic trajectories has attracted a lot of attentions in recent years. Most existing work discover the similar behavior of moving objects through analysis of their semantic trajectory pattern, that is, sequences of locations. However, this kind of trajectories without considering the duration of staying on a location limits wild applications. For example, Tom and Anne have a common pattern of Home→Restaurant → Company → Restaurant, but they are not similar, since Tom works at Restaurant, sends snack to someone at Company and return to Restaurant while Anne has breakfast at Restaurant, works at Company and has lunch at Restaurant. If we consider duration of staying on each location we can easily to differentiate their behaviors. In this paper, we propose a novel approach for discovering common behaviors by considering the duration of staying on each location of trajectories (DoSTra). Our approach can be used to detect the group that has similar lifestyle, habit or behavior patterns and predict the future locations of moving objects. We evaluate the experiment based on synthetic dataset, which demonstrates the high effectiveness and efficiency of the proposed method.

Efficient detection of emergency event from moving object data streams

The advance of positioning technology enables us to online collect moving object data streams for many applications. One of the most significant applications is to detect emergency event through observed abnormal behavior of objects for disaster prediction. However, the continuously generated moving object data streams are often accumulated to a massive dataset in a few seconds and thus challenge existing data analysis techniques. In this paper, we model a process of emergency event forming as a process of rolling a snowball, that is, we compare a size-rapidly-changed (e.g., increased or decreased) group of moving objects to a snowball. Thus, the problem of emergency event detection can be resolved by snowball discovery. Then, we provide two algorithms to find snowballs: a clustering-and-scanning algorithm with the time complexity of O(n 2) and an efficient adjacency-list-based algorithm with the time complexity of O(nlogn). The second method adopts adjacency lists to optimize efficiency. Experiments on both real-world dataset and large synthetic datasets demonstrate the effectiveness, precision and efficiency of our algorithms © 2014 Springer International Publishing Switzerland.

Real-time traffic flow statistical analysis based on network-constrained moving object trajectories

In this paper, we propose a novel traffic flow analysis method, Network-constrained Moving Objects Database based Traffic Flow Statistical Analysis (NMOD-TFSA) model. By sampling and analyzing the spatial-temporal trajectories of network constrained moving objects, NMOD-TFSA can get the real-time traffic conditions of the transportation network. The experimental results show that, compared with the floating-car methods which are widely used in current traffic flow analyzing systems, NMOD-TFSA provides an improved performance in terms of communication costs and statistical accuracy.

Comparing shape and temporal PDMs

The Point Distribution Model (PDM) has been successfully used in representing sets of static and moving images. A recent extension to the PDM for moving objects, the temporal PDM, has been proposed. This utilises quantities such as velocity and acceleration to more explicitly consider the characteristics of the movement and the sequencing of the changes in shape that occur. This research aims to compare the two types of model based on a series of arm movements, and to examine the characteristics of both approaches.

Multiple-object tracking in cluttered and crowded public spaces

This paper addresses the problem of tracking moving objects of variable appearance in challenging scenes rich with features and texture. Reliable tracking is of pivotal importance in surveillance applications. It is made particularly difficult by the nature of objects encountered in such scenes: these too change in appearance and scale, and are often articulated (e.g. humans). We propose a method which uses fast motion detection and segmentation as a constraint for both building appearance models and their robust propagation (matching) in time. The appearance model is based on sets of local appearances automatically clustered using spatio-kinetic similarity, and is updated with each new appearance seen. This integration of all seen appearances of a tracked object makes it extremely resilient to errors caused by occlusion and the lack of permanence of due to low data quality, appearance change or background clutter. These theoretical strengths of our algorithm are empirically demonstrated on two hour long video footage of a busy city marketplace.

Wireless video-based sensor networks for surveillance of residential districts

Compared to traditional wired video sensor networks to supervise a residential district, Wireless Video-based Sensor Networks (WVSN) can provide more detail and precise information while reduce the cost. However, state-of-the-art low cost wireless video-based sensors have very constrained resources such as low bandwidth, small storage, limited processing capability, and limited energy resource. Also, due to the special sensing range of video-based sensors, cluster-based routing is not as effective as it apply to traditional sensor networks. This paper provides a novel real-time change mining algorithm based on an extracted profile model of moving objects learnt from frog's eyes. Example analysis shows the extracted profile would not miss any important semantic images to send to the Base Station for further hazards detection, while efficiently reducing futile video stream data to the degree that nowadays wireless video sensor can realize. Thus it makes WVSN available to surveillance of residential districts.

Towards simulation of flapping wings using immersed boundary method

In this work the immersed boundary method is applied to simulate incompressible turbulent flows around stationary and moving objects. The goal is to demonstrate that the immersed boundary technique along with a large eddy simulation approach is capable of simulating the effect of the so-called leading edge vortex (LEV), which can be found in flapping wing aerodynamics. A Lagrangian method is used to approximatethe solutions in the freshly cleared cells that lay within solid objects at one time step and emerge into fluid domain at the next time step. Flow around a stationary cylinder at ReD D 20, 40, and 3900 (based oncylinder diameter D) is first studied to validate the immersed boundary solver based on the finite volume scheme using a staggered grid. Then, a harmonically oscillating cylinder at ReD D 10 000 is considered to test the solver after the Lagrangian method is implemented to interpolate the solution in the freshly cleared cells. Finally, this approach is used to study flows around a stationary flat-plate at several angles of attack and fast pitching flat-plate. The rapidly pitching plate creates a dynamic LEV that can be used to improve the efficiency of flapping wings of micro air vehicle and to determine the optimum flapping frequency.

Identification of moving obstacles with pyramidal Lucas Kanade optical flow and k means clustering

This paper describes the methodology for identifying moving obstacles by obtaining a reliable and a sparse optical flow from image sequences. Given a sequence of images, basically we can detect two-types of on road vehicles, vehicles traveling in the opposite direction and vehicles traveling in the same direction. For both types, distinct feature points can be detected by Shi and Tomasi corner detector algorithm. Then pyramidal Lucas Kanade method for optical flow calculation is used to match the sparse feature set of one frame on the consecutive frame. By applying k means clustering on four component feature vector, which are to be the coordinates of the feature point and the two components of the optical flow, we can easily calculate the centroids of the clusters and the objects can be easily tracked. The vehicles traveling in the opposite direction produce a diverging vector field, while vehicles traveling in the same direction produce a converging vector field

Moving beyond mateship:reconstructing Australian men's practices

In this chapter I will explore the implications of the definitively Australian
style of masculine behaviour called 'mateship' for gender relations in
Australia. Mateship is part of the Australian male heritage; it originated in
colonial days and was glorified in war and sport. The feminist movement
in Australia has challenged the dominant form of masculinity inherent in
mateship and the basic rationale for gender relations that flow from it. In
this context, I will discuss Australian profeminist men's attempts to challenge patriarchal gender relations and construct non-patriarchal subjectivities and practices. Theorizing about masculinity in Australia has tended to be derivative of overseas literature. This is partly because publishers are looking for overseas markets for their books so they discourage writers on masculinity from grounding men's practices in a specifically Australian context. While there are benefits in generalizing about western masculinities, such writing misses the uniqueness of the lived experiences of Australian men. It is this uniqueness that I will address in this chapter. As McGrane and Patience (1995: 15) note, 'Australian masculinism has a history of its own that needs to be recognized at the same time as it can be usefully compared to the masculinisms of similar cultures'.

Eiffel assertions and the external structure of classes and objects

The “external structure” in an object oriented system refers here to the  graphs of objects and classes. The class structure graph or class model is derived from the object structure graph or object model, and in this operation structural information is lost, or never made explicit. Although object oriented programming languages capture the class model as declarations,  contradictory assumptions about object model properties may be made introducing faults into the design. Consistent assumptions about the object model can be specified in the code using assertions such as Eiffel’s  invariants, preconditions and postconditions. Three examples specifying the external structure are considered.

Performance and narrative, bodies and movement in the construction of places and objects, spaces and knowledges: The case of the Maltese megaliths

The article explores the ways knowledge and space are co-produced performatively through bodily movement in an examination of the Maltese megaliths the first complex stone structures in the world. It is argued that knowledge is best seen as spatialized narratives of human actions and objects as materialized forms of those spatial narratives. Rewriting our social and historical narratives so that the performativity of place, space and knowledge is restored opens new possibilities for rethinking the social and material order.

A token-based independent update protocol for managing replicated objects

This paper presents the design and evaluation of a token-based protocol supporting independent updates of replicated objects. The paper makes three major contributions. Firstly, a token is used to simplify the control of update propagation and ordering. Secondly a partial commit state is introduced to support independent updates and to enhance the efficiency of transaction processing. Thirdly, a data partition methodology based on priorities is adopted to reduce update conflicts. We evaluate our protocol in comparison of two existing replication-control protocols in terms of transaction processing efficiency.