2006 IEEE International Conference on Video and Signal Based Surveillance最新文献

{"title":"Signal Based Node Activation in Wireless Sensor Networks","authors":"R. Tynan, Gregory M. P. O'Hare, A. Ruzzelli","doi":"10.1109/AVSS.2006.104","DOIUrl":"https://doi.org/10.1109/AVSS.2006.104","url":null,"abstract":"The emerging technology of Wireless Sensor Networks offers the potential for a new and unparalleled level of surveillance for events occurring in an environment. The quality of surveillance will be directly related to the quantity and density of sensors that can be deployed, and that are actively participating in the monitoring at any given instant. In opposition to this is the requirement for judicious power management of the network in order to prolong its operational lifespan by minimizing node activity. In this paper we explore the effect on surveillance quality when using interpolation error as a mechanism for deciding node activations. More specifically we examine the effect of using this signal based hibernation technique on target localization error and node activity.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115749696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Auto-Calibration from Pedestrians","authors":"Imran N. Junejo, H. Foroosh","doi":"10.1109/AVSS.2006.99","DOIUrl":"https://doi.org/10.1109/AVSS.2006.99","url":null,"abstract":"The knowledge of camera intrinsic and extrinsic parameters is useful, as it allows us to make world measurements. Unfortunately, calibration information is rarely available in video surveillance systems and it is difficult to obtain once the system is installed. Auto-calibrating cameras using moving objects (humans) has recently attracted a lot of interest. Two methods are proposed by Lv-Nevatia(2002) and Krahnstoever-Mendonca(2005). The inherent difficulty of the problem lies in the noise that is generally present in the data. We propose a robust and a general linear solution to the problem by adopting a formulation different from the existing methods. The uniqueness of formulation lies in recognizing two harmonic homologies present in the geometry obtained by observing pedestrians, and then using properties of these homologies to obtain linear constraints on the unknown camera parameters. Experiments with synthetic as well as on real data are presented - indicating the practicality of the proposed system.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122177221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D Human Motion Analysis in Monocular Video Techniques and Challenges","authors":"C. Sminchisescu","doi":"10.1109/AVSS.2006.3","DOIUrl":"https://doi.org/10.1109/AVSS.2006.3","url":null,"abstract":"Human motion and action analysis in video is an actively growing field with a broad spectrum of applications including video browsing and indexing, entertainment, virtual reality, human-computer interaction and surveillance. However, human motion analysis systems face important scientific and computational challenges. The proportions of the human body vary across individuals due to gender, weight, age or race. Aside from this variability, any single human body has many degrees of freedom due to articulation and the individual limbs are deformable due to muscle and clothing. Finally many realworld scenes involve multiple interacting humans occluded by each other or by other objects, and the scene conditions may also vary due to the camera motion or lighting changes. These factors make accurate 3D human models difficult to build and difficult to reconstruct reliably from 'flat' 2D images. During this talk I will discuss learning and inference algorithms for estimating 3D human motion in monocular video. While the problem has been traditionally approached using the powerful machinery of generative models, operating in an analysis by synthesis loop, the main emphasis of this talk will be on an emerging class of complementary discriminative temporal estimation models. These can be viewed as upside down, bottom-up versions of classical temporal models used with Kalman filtering or particle filtering. But instead of inverting a generative imaging model, we will learn to cooperatively predict complex, feedforward 2D-to-3D mappings, using Conditional Bayesian Mixtures of Experts. These are embedded in a probabilistic temporal framework in order to enforce dynamic constraints and allow a principled propagation of uncertainty. We call the resulting model BM3E (a Conditional Bayesian Mixture of Experts Markov Model). During the talk, I will discuss how inference can be restricted, for efficiency, to low-dimensional non-linear state spaces, and how the framework can be extended in order to deal with clutter and occlusion. I will also discuss the relative advantages of generative and initialize and recover from failure.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115864742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Behavior Recognition in Human Object Interactions with a Task Model","authors":"Weilie Yi, D. Ballard","doi":"10.1109/AVSS.2006.28","DOIUrl":"https://doi.org/10.1109/AVSS.2006.28","url":null,"abstract":"Behavior recognition can be greatly levitated by a model of the task with which the behavior is associated. We present a Markovian task model that captures the temporal relations between subtasks, and provides prior information that helps behavior recognition from simple and noisy sensory data. A Dynamic Bayesian Network is used to integrate multi-model evidence and infer underlying task states. Experiments demonstrate that this system can recognize human behavior in everyday tasks such as making sandwiches.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124651315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparison on Histogram Based Image Matching Methods","authors":"W. Jia, Huaifeng Zhang, Xiangjian He, Qiang Wu","doi":"10.1109/AVSS.2006.5","DOIUrl":"https://doi.org/10.1109/AVSS.2006.5","url":null,"abstract":"Using colour histogram as a stable representation over change in view has been widely used for object recognition. In this paper, three newly proposed histogram-based methods are compared with other three popular methods, including conventional histogram intersection (HI) method, Wong and Cheung's merged palette histogram matching (MPHM) method, and Gevers' colour ratio gradient (CRG) method. These methods are tested on vehicle number plate images for number plate classification. Experimental results disclose that, the CRG method is the best choice in terms of speed, and the GWHI method can give the best classification results. Overall, the CECH method produces the best performance when both speed and classification performance are concerned.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"344 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115969196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grouped-People Splitting Based on Face Detection and Body Proportion Constraints","authors":"S. Piva, L. Comes, M. Asadi, C. Regazzoni","doi":"10.1109/AVSS.2006.56","DOIUrl":"https://doi.org/10.1109/AVSS.2006.56","url":null,"abstract":"This paper presents a method based on skin colormodel face detection and human body proportion constraints to estimate the number and position of people entering the monitored scene as a compact group. This helps to split the group into individual persons and solve a common lack in traditional background subtraction based detection and tracking methods: despite common systems, this algorithm can recognize the number and position of the people in a single change detection blob. The hypotheses are: standing subjects, face visibility from the point of view of the camera, a calibrated map to estimate objects' distance from the sensor and to estimate expected people's height on the image plane. These estimations allow dynamic thresholding of several shape parameters and lead to very interesting results.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123440108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust License Plate Detection Using Covariance Descriptor in a Neural Network Framework","authors":"F. Porikli, Tekin Kocak","doi":"10.1109/AVSS.2006.100","DOIUrl":"https://doi.org/10.1109/AVSS.2006.100","url":null,"abstract":"We present a license plate detection algorithm that employs a novel image descriptor. Instead of using conventional gradient filters and intensity histograms, we compute a covariance matrix of low-level pixel-wise features within a given image window. Unlike the existing approaches, this matrix effectively captures both statistical and spatial properties within the window. We normalize the covariance matrix using local variance scores and restructure the unique coefficients into a feature vector form. Then, we feed these coefficients into a multi-layer neural network. Since no explicit similarity or distance computation is required in this framework, we are able to keep the computational load of the detection process low. To further accelerate the covariance matrix extraction process, we adapt an integral image based data propagation technique. Our extensive analysis shows that the detection process is robust against noise, illumination distortions, and rotation. In addition, the presented method does not require careful fine tuning of the decision boundaries.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128411327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An LMI Approach for Reliable PTZ Camera Self-Calibration","authors":"Hongdong Li, Chunhua Shen","doi":"10.1109/AVSS.2006.21","DOIUrl":"https://doi.org/10.1109/AVSS.2006.21","url":null,"abstract":"PTZ (Pan-Tilt-Zoom) cameras are widely used for large-area video surveillance. For many visual tracking and video analysis tasks, an accurate camera calibration is very important. Traditional off-line camera calibration algorithms are often not satisfactory because some of the PTZ camera intrinsic parameters (e.g., focal length) may change during working. Despite of their theoretical elegance, existing on-line camera self-calibration algorithms are not satisfactory either, because of the lack of numerical stability. This paper proposes a new method based on LMI (linear matrix inequality) optimization. This method automatically incorporates the required positive-definiteness constraint into the computation, thus delivers more reliable and more stable results. Experiments on both synthetic data and real images have validated the advantages of our method.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128423300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Secure Digital Watermarking Scheme for MPEG-2 Video Copyright Protection","authors":"Yuk Ying Chung, F. Xu","doi":"10.1109/AVSS.2006.12","DOIUrl":"https://doi.org/10.1109/AVSS.2006.12","url":null,"abstract":"Video watermarking is an important method of protecting the intellectual property copyright of the video media. It allows embedding of copyright information into the video pictures. In this paper a new hybrid approach of digital video watermarking scheme with an Error Correcting Code (ECC) is proposed. This watermarking scheme maximizes the watermark payload while minimizing the perceptual degradation of video quality caused by the embedded watermark by means of an appropriate choice of embedding position. Two hybrid error correcting codes, BCH(31,8) and Turbo (3,1) with repetition code were implemented and compared. We found that the hybrid approach of BCH(31,8) with repetition code achieved higher error correcting capability than Turbo (3,1) with repetition code under the simulated noise tests.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"8 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126932106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bicoherence Used to Predict Lucky Regions in Turbulence Affected Surveillance","authors":"Zhiying Wen, D. Fraser, A. Lambert","doi":"10.1109/AVSS.2006.29","DOIUrl":"https://doi.org/10.1109/AVSS.2006.29","url":null,"abstract":"Long-distance surveillance is an emerging technology which attracts more and more attention from researchers in different areas, particularly for security applications. One of the major challenges of such technology is how to remove the effect due to atmospheric turbulence to get highresolution images. A common practice is to take shortexposure images of the target so that the effect of atmospheric turbulence is frozen, and then the raw images are post-processed to estimate a good quality image (speckle imaging techniques). A new lucky region approach based on such techniques is presented in this paper. The lucky regions are extracted through bicoherence analysis, and the final output image is obtained by post-processing (dewarping and blind deconvolving) the lucky regions. The new technique allows for automatic identification and extraction of lucky regions, and can be easily implemented. Realworld results show that the algorithm is promising.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127726820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}