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检索条件"主题词=change detection algorithms"

共 1588 条记录，以下是801-810 订阅

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Moving Object Tracking from Videos Based on Enhanced Space-Time-Range Mean Shift and Motion Consistency

Moving Object Tracking from Videos Based on Enhanced Space-T...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Tiesheng Wang Irene Y.H. Gu Andrew Backhouse Pengfei Shi Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Department of Signals and Systems Chalmers University of Technology Gothenburg Sweden

Video surveillance and object tracking have drawn increased interests in recent years. This paper addresses the problem of moving object tracking from image sequences captured from stationary cameras. Based on the previous work on video segmentation using joint space-time-range mean shift, we extend the scheme to enable the tracking of moving objects. Large displacements of pdf modes in consecutive image frames are exploited for tracking. We also improve the above mean shift-based video segmentation by introducing edge-guided merging of over-segmented regions. This can be viewed as an extension of the enhanced mean shift 2D image segmentation in [2] to the enhanced space-time-range mean shift video segmentation. Experiments have been conducted on several indoor and outdoor videos. Our preliminary results and performance evaluation have indicated the effectiveness of the proposed scheme.

关键词： Tracking Videos Image segmentation Merging Kernel Cameras Object detection change detection algorithms Image processing Pattern recognition

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Detecting and Tracking Moving Objects in Sequences of Color Images

Detecting and Tracking Moving Objects in Sequences of Color ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hugh L. Kennedy Pacific Noise and Vibration (PNV) Private Limited Canberra ACT Australia

A statistical change detector, implemented as a zero-latency finite-memory filter, is used to identify anomalies in temporal pixel statistics. An F-distributed test statistic is computed for each pixel and used in a hypothesis test. The tracker, with automatic track initiation and termination, uses a low-complexity pairwise joint probabilistic data association (JPDA) algorithm, which has been restricted to consider clusters (sub-problems) containing no more than two tracks. The track state and clutter model are augmented to include color. The detector and tracker are used to process sample video data.

关键词： Object detection Color Target tracking Detectors Radar tracking Statistics Testing Filters Statistical analysis change detection algorithms

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The Online Cooperating View Maintenance Based on Source View Increment

The Online Cooperating View Maintenance Based on Source View...

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International Conference on Computer Supported Cooperative Work in Design

作者： Hai Liu Yong Tang Qimai Chen Computer Science Department Sun Yat-sen University GuangZhou China Computer Science Department South China Normal University GuangZhou China

ISBN: (纸本)1424409624

View maintenance is an important field in the research of dataware house. This paper provides an online view self-maintenance method based on source view's increment to keep the materialized view consistent with the data source. We use the cooperation between the integrator at warehouse and the monitor at data source to maintenance the view, and the method does not require to query back to data source, it can accelerate view maintenance and lessen the burden of communication between dataware house and data sources.

关键词： Monitoring Warehousing Data warehouses Architecture Collaborative work Computer science Sun Acceleration Data analysis change detection algorithms

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Block-Level Processing of a Video Object Segmentation Algorithm for Real-Time Systems

Block-Level Processing of a Video Object Segmentation Algori...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Jisu Kim Jonghwan Zhu Hyuk-Jae Lee SK Telecom Seoul South Korea School of Electrical Engineering and Computer Science Seoul National University Seoul South Korea

A recently-developed object segmentation algorithm in the work of Kim et al. (2006) achieved real-time generation of object masks with reasonable accuracy. However, this algorithm wastes a memory space and requires unnecessary computations because it uses an entire image frame to extract a video object. This paper modifies the segmentation algorithm according to Kim et al. (2006) to enable block-level processing of video object segmentation. An image frame is partitioned into 8times8 blocks, each of which is processed independently with the segmentation information inherited from its left and upper neighboring blocks as well as the previous frame. Simulation results show that the block-level processing algorithm saves 36% of the memory size and speeds up the computation by 361%. As a result, the proposed algorithm can process QCIF-size video at over 20 fps with ARM7TDMI running at 100 MHz.

关键词： Object segmentation Real time systems Partitioning algorithms Image segmentation Layout Video compression change detection algorithms Object detection Telecommunications Computer science

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Detecting change in Data Stream: Using Sampling Technique

Detecting Change in Data Stream: Using Sampling Technique

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International Conference on Natural Computation (ICNC)

作者： Wei Li Xiaoming Jin Xiaojun Ye School of Software Tsinghua University China

Detecting changes in data streams is an important area of research with many applications in which the underlying generating mechanism of data stream is constantly evolving, not liking the static data that was generated by a fixed process. To understand and study the evolving mechanism of the data stream, detecting the changes in data streams is necessary where the 'change' is defined normally as 'the change of distribution of the streaming data'. In many real-world applications, this problem is challenging. Firstly, only the data stored in a fixed size of memory can be examined, whereas the changes might occur within data items with much bigger size. Moreover, the streaming data arrive at a high speed rate, so if we can not process the data quickly, the detecting of the change will be impossible. In this paper, we attack the issues by introducing a change detection method that utilizing sampling technique. A formal definition of the change in data stream will be given. The approach assumes that the points in the data stream are independently generated, but otherwise no assumptions on the nature of the generating process. The empirical study demonstrates the superiority of our techniques over the existing ones in terms of both efficiency and effectiveness.

关键词： Sampling methods Character generation Testing Application software Data mining Probability distribution change detection algorithms History Telephony Credit cards

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Auditory and visual integration based localization and tracking of humans in daily-life environments

Auditory and visual integration based localization and track...

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2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Hyun-Don Kim Kazunori Komatani Tetsuya Ogata Hiroshi G. Okuno Speech Media Processing Group Kyoto University Kyoto Japan

The purpose of this research is to develop techniques that enable robots to choose and track a desired person for interaction in daily-life environments. Therefore, localizing multiple moving sounds and human faces is necessary so that robots can locate a desired person. For sound source localization, we used a cross-power spectrum phase analysis (CSP) method and showed that CSP can localize sound sources only using two microphones and does not need impulse response data. An expectation-maximization (EM) algorithm was shown to enable a robot to cope with multiple moving sound sources. For face localization, we developed a method that can reliably detect several faces using the skin color classification obtained by using the EM algorithm. To deal with a change in color state according to illumination condition and various skin colors, the robot can obtain new skin color features of faces detected by OpenCV, an open vision library, for detecting human faces. Finally, we developed a probability based method to integrate auditory and visual information and to produce a reliable tracking path in real time. Furthermore, the developed system chose and tracked people while dealing with various background noises that are considered loud, even in the daily-life environments.

关键词： Humans Face detection Skin change detection algorithms Microphones Lighting Robot vision systems Computer vision Libraries Background noise

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A Robust Method for Multiple Face Tracking Using Kalman Filter

A Robust Method for Multiple Face Tracking Using Kalman Filt...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Zaheer Shaik Vijayan Asari Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

ISBN: (纸本)9781424431625

A robust method for tracking faces of multiple people moving in a scene using Kalman filter is proposed in this paper. To distinguish faces of people during partial occlusion the proposed method uses the non-parametric cloth distribution. To overcome the problem of total occlusion, faces are tracked using the values generated by Kalman prediction algorithm. The size, top-left coordinate and velocity of motion of the detected face being the parameters of the Kalman vector; the predicted values are used to locate faces in the next frame. The faces are redetected and the templates are updated at discrete time intervals when the similarity measures, between the faces detected and respective face templates, are less than a preset threshold. Skin segmentation based face detection makes the algorithm computationally simple, and updating the face template makes it invariant to pose changes. The proposed method is experimented to be invariant to lightning conditions, change of pose, and works well in the case of partial and total occlusion for a short period.

关键词： Robustness Face detection Kalman filters Layout Prediction algorithms Motion detection Time measurement Skin change detection algorithms Lightning

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Property Based Intrusion detection to Secure OLSR

Property Based Intrusion Detection to Secure OLSR

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International Conference on Wireless and Mobile Communications (ICWMC)

作者： Frederic Cuppens Nora Cuppens-Boulahia Seila Nuon Tony Ramard GET/ENST de Bretagne France Institut de Technologie du Combodge Phnom Penh Cambodia

ISBN: (纸本)9781424431397;9780769527963

In this paper, we examine security issues related to proactive routing protocols for mobile ad-hoc networks (MANETs). Specifically, we investigate security properties of the optimized link-state routing (OLSR) protocol, a proactive routing protocol for MANETs. We analyze the possible attacks against the integrity of the network routing infrastructure, and present techniques to counter some attacks. Our main approach is based on a formal model to describe normal and incorrect node behaviors. This model allows us to derive security properties. The algorithm checks if these security properties are violated. If they are, detection occurs to allow the normal node to find a path without incorrect node behavior. Our approach does not change the message format, so it is very easy to implement the corresponding algorithm. While we use OLSR as an example of protocol for our studies, we argue that the presented techniques apply equally to any proactive routing protocol for MANETs.

关键词： Intrusion detection Routing protocols Mobile ad hoc networks Ad hoc networks Network topology Communication system traffic control change detection algorithms Mechanical factors Bidirectional control Counting circuits

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An Improvement on Precision in DDoS Source-End detection with Multi-stream Combined HMM

An Improvement on Precision in DDoS Source-End Detection wit...

引用

International Conference on Computational Intelligence and Security Workshops (CISW)

作者： Jian Kang Qiang Li Yuan Zhang Zhuo Li Department of Computer Science & Technology Jilin University Changchun China

DDoS (distributed denial-of-service) attacks detection system deployed in source-end network is superior in perceiving and throttling attacks before dataflows enter Internet, comparing with that in victim network. However, the current existed works in source- end network are so fragile, lead to a high false-positive rate and false-negative rate. This paper proposes a novel approach using multi-stream combined hidden Markov model (MC-HMM) on source-end DDoS detection for integrating multi-features simultaneously. The multi-features include the S-D-P three-tuple, TCP header Flags, and IP header ID field. Through experiments, we compared our original approach based on multiple detection features with other algorithms (such as CUSUM and HMM). The results present that our approach effectively reduces false-positive rate and false-negative rate, and improves the precision of detection.

关键词： Hidden Markov models TCPIP Packaging Computer crime change detection algorithms Data mining Computational intelligence Computer security Computer science IP networks

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Detecting Primary Transmitters via Cooperation and Memory in Cognitive Radio

Detecting Primary Transmitters via Cooperation and Memory in...

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Annual Conference on Information Sciences and Systems (CISS)

作者： A. Betran-Martinez O. Simeone Y. Bar-Ness CWCSPR New Jersey Institute of Technology Newark NJ USA

Effective detection of the activity of primary transmitters is known to be one of the major challenges to the implementation of cognitive radio systems. In this paper, we investigate the use of cooperation and memory (using tools from change detection) as means to enhance primary detection. We focus on the simple case of two secondary users and one primary source. Numerical results show the relevant performance benefits of both cooperation and memory.

关键词： Radio transmitters Cognitive radio System performance Quality of service Interference Fading Relays change detection algorithms Gaussian approximation Protocols

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