18395453
Content-Based Model Template Adaptation and Real-Time System for Behavior Interpretation in Sports Video
In this paper, we present a real-time sports analysis system, which not only recognizes the semantic events, but also concludes the behavior, like player's tactics. To this end, we propose an advanced multiple-player tracking algorithm, which addresses two improvements on practical problems: (1) updating of the player template so that it remains a good model over time, and (2) adaptive scaling of the template size depending on the player motion. In this algorithm, we obtain the initial locations of players in the first frame. The tracking is performed by considering both the kinematic constraints of the player and the color distribution of appearance, thereby achieving promising results. We demonstrate the performance of the proposed system by evaluating it for double tennis matches where the player count and the resulting occlusions are challenging.
2286606
Scene-Level Analysis for Tennis Sports Video using Weighted Linear Combination of Visual Cues
We present a scene-level analysis system that takes TV broadcasted tennis footage as input and produces a behavior analysis of the moving-players in the scene. To achieve this functionality, our algorithm relies on two modular blocks. The first one detects and tracks a number of key objects in the image domain, like moving-players and the playing-field. Afterwards, a camera-calibration algorithm is applied that uses the lines of the court as a reference and transforms image coordinates to physical positions to compute the camera parameters. The second block firstly models several important events of a tennis game, such as service and net-approach, based on four real-world visual features provided by the first block. This paper proposes a new improved model, since it weights the importance of each visual cue to different events, rather than providing a simple combination of these four visual cues as was done in previous work. Based on the new model, we can accurately determine what kind of event the current input frame belongs to. Furthermore, we detect the start time and end time of each event using a simple but efficient temporal filter. Our proposed system is capable of classifying each tennis play into three semantic categories, which are popular and familiar to most viewers. This paper presents details of the system, together with results on a number of tennis video sequences.
17887061
Popularity Biased Hybrid Search in P2P Systems
Hybrid search, which combines the flooding and DHT techniques, has been employed for efficient content-locating in peer-to-peer (P2P) systems. Choosing a flooding or a DHT subjects to the population of desired data. However, existing studies summarizes the popularity only based on local information, or does not take into account the dynamic factor of P2P systems. Thus, the users suffer from long search latency when performing hybrid search due to inaccurate estimations of the resource popularity. In this paper, we propose a popularity biased hybrid search method in systems. Our model allows the P2P systems dynamically detect the content popularity, and accurately make a decision of search methods. In this way, our work effectively reduces the query traffic cost and response time. The result our trace-driven simulations report shows that our model outperforms existing approaches in search efficiency
8876000
Discovery of Multiple-Level Association Rules from Large Databases
Previous studies on mining association rules find rules at single concept level, however, mining association rules at multiple concept levels may lead to the discovery of more specific and concrete knowledge from data. In this study, a top-down progressive deepening method is developed for mining multiplelevel association rules from large transaction databases by extension of some existing association rule mining techniques. A group of variant algorithms are proposed based on the ways of sharing intermediate results, with the relative performance tested on different kinds of data. Relaxation of the rule conditions for finding “level-crossing” association rules is also discussed in the paper.
5983751
Fusion of color and infrared video for moving human detection
We approach the task of human silhouette extraction from color and thermal image sequences using automatic image registration. Image registration between color and thermal images is a challenging problem due to the difficulties associated with finding correspondence. However, the moving people in a static scene provide cues to address this problem. In this paper, we propose a hierarchical scheme to automatically find the correspondence between the preliminary human silhouettes extracted from synchronous color and thermal image sequences for image registration. Next, we discuss strategies for probabilistically combining cues from registered color and thermal images for improved human silhouette detection. It is shown that the proposed approach achieves good results for image registration and human silhouette extraction. Experimental results also show a comparison of various sensor fusion strategies and demonstrate the improvement in performance over non-fused cases for human silhouette extraction.
15978422
TruGrid: a self-sustaining trustworthy grid
In typical distributed environments, there are two parties: consumers and providers. Consumers have computational jobs while may lack of computational resources. Providers have relatively underutilized resources. With the rapid advancement in high-speed networks, how to enable the effective and secure interaction between consumers and providers has become increasingly important. Because of the distributed ownership of resources, however, it is a very challenging problem. We propose the TruGrid, a self-sustaining trustworthy grid, to solve it. The TruGrid is a novel loosely connected grid system, which guarantees the incentive of every participant and therefore it is a self-sustaining system. We also identify two security challenges arising in the TruGrid, i.e., free-rider and boaster. The proposed trustworthiness management and the penalty model successfully overcome the challenges. Simulation results demonstrate that the TruGrid is a promising actuator to enable the interaction between autonomous consumers and providers.
11822641
Designing for increased software maintainability
The recording and management of design rationales and design decisions are important issues in achieving better support for software maintenance. In the context of existing work on design rationale models, this paper introduces an approach to the integration of system design, design decisions and design rationales and the formalisation of design decisions in terms of software artifacts and their relationships. The relationships and properties characterising design decisions capture precisely the intent of these decisions, and can be checked and enforced to ensure that the decisions are followed and maintained in the actual development. We also show how the formalised design decisions can be used in better supporting software comprehension and maintenance