The Experts below are selected from a list of 36417 Experts worldwide ranked by ideXlab platform
Yuanfang Wang - One of the best experts on this subject based on the ideXlab platform.
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human activity detection and recognition for video Surveillance
International Conference on Multimedia and Expo, 2004Co-Authors: Wei Niu, Jiao Long, Dan Han, Yuanfang WangAbstract:We present a framework for detecting and recognizing human activities for outdoor video Surveillance applications. Our research makes the following contributions: For activity detection and tracking, we improve robustness by providing intelligent control and fail-over mechanisms, built on top of low-level motion detection algorithms such as frame differencing and feature correlation. For activity recognition, we propose an efficient representation of human activities that enables recognition of different interaction patterns among a group of people based on simple statistics computed on the tracked trajectories, without building complicated Markov chain, hidden Markov models (HMM), or coupled hidden Markov models (CHMM). We demonstrate our techniques using real-world video data to automatically distinguish normal behaviors from suspicious ones in a parking lot setting, which can aid Security Surveillance
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multi camera spatio temporal fusion and biased sequence data learning for Security Surveillance
ACM Multimedia, 2003Co-Authors: Long Jiao, Yuanfang Wang, Edward Y ChangAbstract:We present a framework for multi-camera video Surveillance. The framework consists of three phases: detection, representation, and recognition. The detection phase handles multi-source spatio-temporal data fusion for efficiently and reliably extracting motion trajectories from video. The representation phase summarizes raw trajectory data to construct hierarchical, invariant, and content-rich descriptions of the motion events. Finally, the recognition phase deals with event classification and identification on the data descriptors. Because of space limits, we describe only briefly how we detect and represent events, but we provide in-depth treatment on the third phase: event recognition. For effective recognition, we devise a sequence-alignment kernel function to perform sequence data learning for identifying suspicious events. We show that when the positive training instances (i.e., suspicious events) are significantly outnumbered by the negative training instances (benign events), then SVMs (or any other learning methods) can suffer a high incidence of errors. To remedy this problem, we propose the kernel boundary alignment (KBA) algorithm to work with the sequence-alignment kernel. Through empirical study in a parking-lot Surveillance setting, we show that our spatio-temporal fusion scheme and biased sequence-data learning method are highly effective in identifying suspicious events.
Hyun Myung - One of the best experts on this subject based on the ideXlab platform.
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weighted joint based human behavior recognition algorithm using only depth information for low cost intelligent video Surveillance system
Expert Systems With Applications, 2016Co-Authors: Hanguen Kim, Sangwon Lee, Youngjae Kim, Serin Lee, Dongsung Lee, Hyun MyungAbstract:Human joint estimation and behavior recognition algorithms are presented.Only depth information is used and can be executed on a low cost computing platform.The proposed system can be used with any subject instantly without pre-calibration.Experiments to verify the proposed algorithms have been conducted. Recent advances in 3D depth sensors have created many opportunities for Security, Surveillance, and entertainment. The 3D depth sensors provide more powerful monitoring systems for dangerous situations irrespective of lighting conditions in buildings or production facilities. To robustly recognize emergency actions or hazardous situations of workers at a production facility, we present human joint estimation and behavior recognition algorithms that solely use depth information in this paper. To estimate human joints on a low cost computing platform, we propose a human joint estimation algorithm that integrates a geodesic graph and a support vector machine (SVM). The human feature points are extracted within a range of geodesic distance from a geodesic graph. The geodesic graph is used for optimizing the estimation result. The SVM-based human joint estimator uses randomly selected human features to reduce computation. Body parts that typically involve many motions are then estimated by the geodesic distance value. The proposed algorithm can work for any human without calibration, and thus the system can be used with any subject immediately even with a low cost computing platform. In the case of the behavior recognition algorithm, the algorithm should have a simple behavior registration process, and it also should be robust to environmental changes. To meet these goals, we propose a template matching-based behavior recognition algorithm. Our method creates a behavior template set that consists of weighted human joint data with scale and rotation invariant properties. A single behavior template consists of the joint information that is estimated per frame. Additionally, we propose adaptive template rejection and a sliding window filter to prevent misrecognition between similar behaviors. The human joint estimation and behavior recognition algorithms are evaluated individually through several experiments and the performance is proven through a comparison with other algorithms. The experimental results show that our method performs well and is applicable in real environments.
L.d. Nguyen - One of the best experts on this subject based on the ideXlab platform.
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Secured wireless digital video Surveillance for distributed enterprises
IEEE 60th Vehicular Technology Conference 2004. VTC2004-Fall. 2004, 2026Co-Authors: L.d. NguyenAbstract:If an organization has more than one office, it can be extremely difficult to set up a coordinated wireless camera Surveillance system that delivers timely information about what is happening at each site. This is simply because most of today's wireless Security Surveillance technology was not designed to monitor more than a single building. This paper lays out the EncryptedEye/spl trade/ solution using WebOffice's technology to successfully solve challenges in this current environment. EncryptedEye/spl trade/ is a scalable, innovative, and affordable digital Surveillance solution that solves Surveillance challenges of: (1) wireless environment, (2) moving vast video recording files securely and reliably to a centralized location for back up, archiving and analysis, (3) rapid deployment with low TCO, and (4) securely viewing everything and anywhere.
Sevgi Zubeyde Gurbuz - One of the best experts on this subject based on the ideXlab platform.
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micro doppler based human activity classification using the mote scale bumblebee radar
IEEE Geoscience and Remote Sensing Letters, 2015Co-Authors: Bahri Cagliyan, Sevgi Zubeyde GurbuzAbstract:Human activity recognition is an emerging technology for many Security, Surveillance, and health service applications utilizing wireless sensor networks (WSNs). However, the exploitation of radar in WSNs has been only recently made possible through the development of small, low-power, and low-cost wireless radar motes, such as the BumbleBee radar developed by the Samraksh Company. This letter explores the capacity of using the BumbleBee radar for indoor human activity classification based on micro-Doppler signatures. The electromagnetic measurements of the signal transmitted by the BumbleBee radar are made to fully characterize the sensor and its limitations. A database of the multiperspective micro-Doppler signatures measured from the BumbleBee radar is compiled to analyze the classification performance and limitations due to the dwell time and the aspect angle. Within the operational constraints delineated, it is shown that the BumbleBee radar can be used to discriminate between walking, running, and crawling, even under variable conditions.
Wen-hsiang Tsai - One of the best experts on this subject based on the ideXlab platform.
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Real-Time Security Monitoring Around a Video Surveillance Vehicle With a Pair of Two-Camera Omni-Imaging Devices
IEEE Transactions on Vehicular Technology, 2011Co-Authors: Pei-hsuan Yuan, Kuo-feng Yang, Wen-hsiang TsaiAbstract:A pair of two-camera omni-imaging devices is designed for use on the roof of a video Surveillance vehicle, and corresponding 3-D vision-based techniques for real-time Security Surveillance around the vehicle are proposed, which may be used to monitor passing-by persons around the vehicle. First, the design of the pair of two-camera omni-imaging devices, each device consisting of two omnicameras, with their optical axes vertically aligned, is described. Then, a new analytic technique for fast 3-D space data acquisition that is based on a panomapping method for image-to-world space transformation, as well as the rotational invariance property of the omni-image, is proposed. Techniques for constructing top- and perspective-view images for the convenient observation of the monitored environment are also proposed. Finally, 3-D vision techniques for automatically detecting passing-by persons and computing their locations and body heights are proposed, followed by experimental results, which show the precision and feasibility of the proposed techniques.