17811625
A Graph-Based Approach for Modeling and Indexing Video Data
In this work, we propose new graph-based data model and indexing to organize and manage video data. To consider spatial and temporal characteristics of video, we introduce a new graph-based data model called spatio-temporal region graph (STRG). Unlike existing graph-based data structures which provide only spatial features, the proposed STRG further provides temporal features, which represent temporal relationships among spatial objects. The STRG is decomposed into its subgraphs object graphs (OGs) and background graphs (BGs). In addition, a new distance measure, called extended graph edit distance (EGED), is introduced in metric space for matching and indexing. Based on clustering and EGED, we propose a new indexing method STRG-index, which is faster and more accurate. We compare the STRG-Index with the M-tree, which is a popular tree-based indexing method for multimedia data. The STRG-index outperforms the M-tree in terms of cost and speed
12522566
Real Time Video Data Mining for Surveillance Video Streams
We extend our previous work [1] of the general framework for video data mining to further address the issue such as how to mine video data using motions in video streams. To extract and characterize these motions, we use an accumulation of quantized pixel differences among all frames in a video segment. As a result, the accumulated motions of segment are represented as a two dimensional matrix. Further, we develop how to capture the location of motions occurring in a segment using the same matrix generated for the calculation of the amount. We study how to cluster those segmented pieces using the features (the amount and the location of motions) we extract by the matrix above. We investigate an algorithm to find whether a segment has normal or abnormal events by clustering and modeling normal events, which occur mostly. In addition to deciding normal or abnormal, the algorithm computes Degree of Abnormality of a segment, which represents to what extent a segment is distant to the existing segments in relation with normal events. Our experimental studies indicate that the proposed techniques are promising.
40603353
3D GIS for Geo-coding Human Activity in Micro-scale Urban Environments
The study of human activity and movement in space and time has been an important research area in the social sciences. However, the difficulty in collecting and analyzing the space-time activity (STA) data using current 3D location positioning techniques has limits in applying the time-geographic and activity theory to transportation and urban research and analyses. This paper develops a “3D Indoor Geo-Coding” technique to identify 3D indoor locational data for analyzing human activities. For implementing 3D positioning methods, this paper presents (1) to model micro-scale urban environments to be a frame of reference (reference data) to identify distance and direction, in order to obtain locational data, and (2) to develop a 3D indoor geo-coding method to identify locational data on individual activities based on the reference data. Finally, this paper describes the output from implementing the 3D indoor geo-coding technique to demonstrate the potential benefits of the 3D indoor geo-coding technique for improving the speed of emergency response using GIS data of the study area at Minnesota State University-Mankato, MN (USA).
16948552
Structured Circuit Semantics for Reactive Plan Execution Systems
A variety of reactive plan execution systems have been developed in recent years, each attempting to solve the problem of taking reasonable courses of action fast enough in a dynamically changing world. Comparing these competing approaches, and collecting the best features of each, has been problematic because of the diverse representations and (sometimes implicit) control structures that they have employed. To rectify this problem, we have extended the circuit semantics notion of teleo-reactive programs into richer, yet compact semantics, called structured circuit semantics (SCS), that can be used to explicitly represent the control behavior of various reactive execution systems. By transforming existing systems into SCS, we can identify underlying control assumptions and begin to identify more rigorously the strengths and limitations of these systems. Moreover, SCS provides a basis for constructing new reactive execution systems, with more understandable semantics, that can be tailored to particular domain needs.
14452578
Automatically mapping code on an intelligent memory architecture
This paper presents an algorithm to automatically map code on a generic intelligent memory system that consists of a host processor and a simpler memory processor. To achieve high performance with this type of architecture, code needs to be partitioned and scheduled such that each section is assigned to the processor on which it runs most efficiently. In addition, the two processors should overlap their execution as much as possible. With our algorithm, applications are mapped fully automatically using both static and dynamic information. Using a set of standard applications and a simulated architecture, we show average speedups of 1.7 for numerical applications and 1.3 for nonnumerical applications over a single host with plain memory. The speedups are very close and often higher than ideal speedups on a more expensive multiprocessor system composed of two identical host processors. Our work shows that heterogeneity can be cost-effectively exploited and represents one step toward effectively mapping code on intelligent memory systems.
1519617
Distributed parallel data storage systems: a scalable approach to high speed image servers
We have designed, built, and analyzed a distributed parallel storage system that will supply image streams fast enough to permit multi-user, “real-time”, video-like applications in a wide-area ATM network-based Internet environment. We have based the implementation on user-level code in order to secure portability; we have characterized the performance bottlenecks arising from operating system and hardware issues, and based on this have optimized our design to make the best use of the available performance. Although at this time we have only operated with a few classes of data, the approach appears to be capable of providing a scalable, high-performance, and economical mechanism to provide a data storage system for several classes of data (including mixed multimedia streams), and for applications (clients) that operate in a high-speed network environment.
19612327
Instruction level redundant number computations for fast data intensive processing in asynchronous processors
Instruction level parallelism (ILP) is strictly limited by various dependencies. In particular, data dependency is a major performance bottleneck of data intensive applications. In this paper we address acceleration of the execution of instruction codes serialized by data dependencies. We propose a new computer architecture supporting a redundant number computation at the instruction level. To design and implement the scheme, an extended data-path and additional instructions are also proposed. The architectural exploitation of instruction level redundant number computations (IL-RNC) makes it possible to eliminate carry propagations. As a result execution of instructions which are serialized due to inherent data dependencies is accelerated. Simulations have been performed with data intensive processing benchmarks and the proposed architecture shows about a 1.2-1.35 fold speedup over a conventional counterpart. The proposed architecture model can be used effectively for data intensive processing in a microprocessor, a digital signal processor and a multimedia processor.