ACM Transactions on Spatial Algorithms and Systems 

About: ACM Transactions on Spatial Algorithms and Systems is an academic journal published by Association for Computing Machinery. The journal publishes majorly in the area(s): Computer science & Medicine. It has an ISSN identifier of 2374-0353. Over the lifetime, 167 publications have been published receiving 2625 citations. The journal is also known as: Spatial algorithms and systems & ACM TSAS.

Time Series analysis by state space methods

Abstract: This excellent text provides a comprehensive treatment of the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbence [sic] terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. The book provides an excellent source for the development of practical courses on time series analysis.

A Server-Assigned Spatial Crowdsourcing Framework

Abstract: With the popularity of mobile devices, spatial crowdsourcing is rising as a new framework that enables human workers to solve tasks in the physical world. With spatial crowdsourcing, the goal is to crowdsource a set of spatiotemporal tasks (i.e., tasks related to time and location) to a set of workers, which requires the workers to physically travel to those locations in order to perform the tasks. In this article, we focus on one class of spatial crowdsourcing, in which the workers send their locations to the server and thereafter the server assigns to every worker tasks in proximity to the worker’s location with the aim of maximizing the overall number of assigned tasks. We formally define this maximum task assignment (MTA) problem in spatial crowdsourcing, and identify its challenges. We propose alternative solutions to address these challenges by exploiting the spatial properties of the problem space, including the spatial distribution and the travel cost of the workers. MTA is based on the assumptions that all tasks are of the same type and all workers are equally qualified in performing the tasks. Meanwhile, different types of tasks may require workers with various skill sets or expertise. Subsequently, we extend MTA by taking the expertise of the workers into consideration. We refer to this problem as the maximum score assignment (MSA) problem and show its practicality and generality. Extensive experiments with various synthetic and two real-world datasets show the applicability of our proposed framework.

Symbolic Trajectories

Abstract: Due to the proliferation of GPS-enabled devices in vehicles or with people, large amounts of position data are recorded every day and the management of such mobility data, also called trajectories, is a very active research field. A lot of effort has gone into discovering “semantics” from the raw geometric trajectories by relating them to the spatial environment or finding patterns, for example, by data mining techniques. A question is how the resulting “meaningful” trajectories can be represented or further queried.In this article, we propose a systematic study of annotated trajectory databases. We define a very simple generic model called symbolic trajectory to capture a wide range of meanings derived from a geometric trajectory. Essentially, a symbolic trajectory is just a time-dependent label; variants have sets of labels, places, or sets of places. They are modeled as abstract data types and integrated into a well-established framework of data types and operations for moving objects. Symbolic trajectories can represent, for example, the names of roads traversed obtained by map matching, transportation modes, speed profile, cells of a cellular network, behaviors of animals, cinemas within 2km distance, and so forth. Symbolic trajectories can be combined with geometric trajectories to obtain annotated trajectories.Besides the model, the main technical contribution of the article is a language for pattern matching and rewriting of symbolic trajectories. A symbolic trajectory can be represented as a sequence of pairs (called units) consisting of a time interval and a label. A pattern consists of unit patterns (specifications for time interval and/or label) and wildcards, matching units and sequences of units, respectively, and regular expressions over such elements. It may further contain variables that can be used in conditions and in rewriting. Conditions and expressions in rewriting may use arbitrary operations available for querying in the host DBMS environment, which makes the language extensible and quite powerful.We formally define the data model and syntax and semantics of the pattern language. Query operations are offered to integrate pattern matching, rewriting, and classification of symbolic trajectories into a DBMS querying environment. Implementation of the model using finite state machines is described in detail. An experimental evaluation demonstrates the efficiency of the implementation. In particular, it shows dramatic improvements in storage space and response time in a comparison of symbolic and geometric trajectories for some simple queries that can be executed on both symbolic and raw trajectories.

Intelligent Intersection: Two-stream Convolutional Networks for Real-time Near-accident Detection in Traffic Video

Abstract: Camera-based systems are increasingly used for collecting information on intersections and arterials. Unlike loop controllers that can generally be only used for detection and movement of vehicles, cameras can provide rich information about the traffic behavior. Vision-based frameworks for multiple-object detection, object tracking, and near-miss detection have been developed to derive this information. However, much of this work currently addresses processing videos offline. In this article, we propose an integrated two-stream convolutional networks architecture that performs real-time detection, tracking, and near-accident detection of road users in traffic video data. The two-stream model consists of a spatial stream network for object detection and a temporal stream network to leverage motion features for multiple-object tracking. We detect near-accidents by incorporating appearance features and motion features from these two networks. Further, we demonstrate that our approaches can be executed in real-time and at a frame rate that is higher than the video frame rate on a variety of videos collected from fisheye and overhead cameras.

Classification of Passes in Football Matches Using Spatiotemporal Data

Abstract: A knowledgeable observer of a game of football (soccer) can make a subjective evaluation of the quality of passes made between players during the game, such as rating them as Good, OK, or Bad. In this article, we consider the problem of producing an automated system to make the same evaluation of passes and present a model to solve this problem.Recently, many professional football leagues have installed object tracking systems in their stadiums that generate high-resolution and high-frequency spatiotemporal trajectories of the players and the ball. Beginning with the thesis that much of the information required to make the pass ratings is available in the trajectory signal, we further postulated that using complex data structures derived from computational geometry would enable domain football knowledge to be included in the model by computing metric variables in a principled and efficient manner. We designed a model that computes a vector of predictor variables for each pass made and uses machine learning techniques to determine a classification function that can accurately rate passes based only on the predictor variable vector.Experimental results show that the learned classification functions can rate passes with 90.2% accuracy. The agreement between the classifier ratings and the ratings made by a human observer is comparable to the agreement between the ratings made by human observers, and suggests that significantly higher accuracy is unlikely to be achieved. Furthermore, we show that the predictor variables computed using methods from computational geometry are among the most important to the learned classifiers.