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2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance最新文献

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Combining Retrieval and Classification for Real-Time Face Recognition 结合检索和分类实现实时人脸识别
2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.26
Giovanni Fusco, Nicoletta Noceti, F. Odone
{"title":"Combining Retrieval and Classification for Real-Time Face Recognition","authors":"Giovanni Fusco, Nicoletta Noceti, F. Odone","doi":"10.1109/AVSS.2012.26","DOIUrl":"https://doi.org/10.1109/AVSS.2012.26","url":null,"abstract":"In this paper we propose a real time face recognition method that combines face matching and identity verification modules in a feedback loop, exploiting the temporal efficiency of matching and the performances of SVM classifiers. Our approach represents an ad-hoc solution for settings characterized by variable quantity, quality and distribution of labeled data among the identities. We assess the procedure on two data sets of different complexities, showing the effectiveness of our solution. For its intrinsic peculiarities and its limited computational cost the method finds application in real time systems, and will be implemented on a wearable device for supporting visually impaired people to localize known faces.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130715056","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}
引用次数: 7
Multiple Person Tracking by Spatiotemporal Tracklet Association 基于时空轨迹关联的多人跟踪
2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.89
Weizhi Nie, Anan Liu, Yuting Su
{"title":"Multiple Person Tracking by Spatiotemporal Tracklet Association","authors":"Weizhi Nie, Anan Liu, Yuting Su","doi":"10.1109/AVSS.2012.89","DOIUrl":"https://doi.org/10.1109/AVSS.2012.89","url":null,"abstract":"In the field of video surveillance, multiple object tracking is a challenging problem in the real application. In this paper, we propose a multiple object tracking method by spatiotemporal tracklet association. Firstly, reliable tracklets, the fragments of the entire trajectory of individual object movement, are generated by frame-wise association between object localization results in the neighbor frames. To avoid the negative influence of occlusion on reliable tracklet generation, part-based similarity computation is performed. Secondly, the produced tracklets are associated considering both spatial and temporal constrains to output the entire trajectory for individual person. Especially, we formulate the task of spatiotemporal multiple tracklet matching into a Maximum A Posterior (MAP) problem in the form of Markov Chain with spatiotemporal context constraints. The experiment on PETS 2012 dataset demonstrates the superiority of the proposed method.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126910401","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}
引用次数: 19
Detection and Summarization of Salient Events in Coastal Environments 沿海环境显著事件的探测与总结
2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.35
Daniel Cullen, J. Konrad, T. Little
{"title":"Detection and Summarization of Salient Events in Coastal Environments","authors":"Daniel Cullen, J. Konrad, T. Little","doi":"10.1109/AVSS.2012.35","DOIUrl":"https://doi.org/10.1109/AVSS.2012.35","url":null,"abstract":"The monitoring of coastal environments is of great interest to biologists and environmental protection organizations with video cameras being the dominant sensing modality. However, it is recognized that video analysis of maritime scenes is very challenging on account of background animation (water reflections, waves) and very large field of view. We propose a practical approach to the detection of three salient events, namely boats, motor vehicles and people appearing close to the shoreline, and their subsequent summarization. Our approach consists of three fundamental steps: region-of-interest (ROI) localization by means of behavior subtraction, ROI validation by means of feature-covariance-based object recognition, and event summarization by means of video condensation. The goal is to distill hours of video data down to a few short segments containing only salient events, thus allowing human operators to expeditiously study a coastal scene. We demonstrate the effectiveness of our approach on long videos taken at Great Point, Nantucket, Massachusetts.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126254589","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}
引用次数: 10
View Invariant Appearance-Based Person Reidentification Using Fast Online Feature Selection and Score Level Fusion 基于快速在线特征选择和分数水平融合的基于视图不变外观的人物再识别
2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.81
M. Eisenbach, Alexander Kolarow, Konrad Schenk, Klaus Debes, H. Groß
{"title":"View Invariant Appearance-Based Person Reidentification Using Fast Online Feature Selection and Score Level Fusion","authors":"M. Eisenbach, Alexander Kolarow, Konrad Schenk, Klaus Debes, H. Groß","doi":"10.1109/AVSS.2012.81","DOIUrl":"https://doi.org/10.1109/AVSS.2012.81","url":null,"abstract":"Fast and robust person reidentification is an important task in multi-camera surveillance and automated access control. We present an efficient appearance-based algorithm, able to reidentify a person regardless of occlusions, distance to the camera, and changes in view and lighting. The use of fast online feature selection techniques enables us to perform reidentification in hyper-real-time for a multi-camera system, by taking only 10 seconds for evaluating 100 minutes of HD-video data. We demonstrate, that our approach surpasses current appearance-based state-of-the-art in reidentification quality and computational speed and sets a new reference in non-biometric reidentification.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133979764","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}
引用次数: 27
Histograms of Optical Flow Orientation for Visual Abnormal Events Detection 用于视觉异常事件检测的光流方向直方图
2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.39
Tian Wang, H. Snoussi
{"title":"Histograms of Optical Flow Orientation for Visual Abnormal Events Detection","authors":"Tian Wang, H. Snoussi","doi":"10.1109/AVSS.2012.39","DOIUrl":"https://doi.org/10.1109/AVSS.2012.39","url":null,"abstract":"In this paper, we propose an algorithm to detect abnormal events based on video streams. The algorithm is based on histograms of the orientation of optical flow descriptor and one-class SVM classifier. We introduce grids of Histograms of the Orientation of Optical Flow (HOFs) as the descriptors for motion information of the monolithic video frame. The one-class SVM, after a learning period characterizing normal behaviors, detects the abnormal events in the current frame. Extensive testing on benchmark dataset corroborates the effectiveness of the proposed detection method.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132663639","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}
引用次数: 63
Neuromorphic Bayesian Surprise for Far-Range Event Detection 远距离事件检测的神经形态贝叶斯惊喜
2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.49
Randolph Voorhies, Lior Elazary, L. Itti
{"title":"Neuromorphic Bayesian Surprise for Far-Range Event Detection","authors":"Randolph Voorhies, Lior Elazary, L. Itti","doi":"10.1109/AVSS.2012.49","DOIUrl":"https://doi.org/10.1109/AVSS.2012.49","url":null,"abstract":"In this paper we address the problem of detecting small, rare events in very high resolution, far-field video streams. Rather than learning color distributions for individual pixels, our method utilizes a uniquely structured network of Bayesian learning units which compute a combined measure of \"surprise\" across multiple spatial and temporal scales on various visual features. The features used, as well as the learning rules for these units are derived from recent work in computational neuroscience. We test the system extensively on both real and virtual data, and show that it out-performs a standard foreground/background segmentation approach as well as a standard visual saliency algorithm.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"376 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133799757","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}
引用次数: 4
An Efficient Background Reconstruction Based Coding Method for Surveillance Videos Captured by Moving Camera 基于背景重构的运动摄像机监控视频编码方法
2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.8
Shumin Han, Xianguo Zhang, Yonghong Tian, Tiejun Huang
{"title":"An Efficient Background Reconstruction Based Coding Method for Surveillance Videos Captured by Moving Camera","authors":"Shumin Han, Xianguo Zhang, Yonghong Tian, Tiejun Huang","doi":"10.1109/AVSS.2012.8","DOIUrl":"https://doi.org/10.1109/AVSS.2012.8","url":null,"abstract":"With the proliferation of moving surveillance cameras, how to effectively compress videos captured from them is becoming more and more important. One significant characteristic is that, these cameras always go and return cyclically within a limited area. Thus we propose to dynamically build up a background frame for each input frame from a generated panorama background and employ it for a background frame based motion compensation to improve the coding efficiency. For the background reconstruction procedure, we firstly extract limited number of feature point pairs between the robustly searched area in the decoded panorama and the current frame. Afterwards, the global motion transformation matrix is obtained to rectify the searched area into a projective plane of the current frame, and then the reconstructed background is produced. Experiments on six in-door and out-door surveillance videos show that, the background reconstruction based coding method achieves significant performance gain.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122267052","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}
引用次数: 2
Splitting Gaussians in Mixture Models 混合模型中的高斯分裂
2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.69
Rubén Heras Evangelio, Michael Pätzold, T. Sikora
{"title":"Splitting Gaussians in Mixture Models","authors":"Rubén Heras Evangelio, Michael Pätzold, T. Sikora","doi":"10.1109/AVSS.2012.69","DOIUrl":"https://doi.org/10.1109/AVSS.2012.69","url":null,"abstract":"Gaussian mixture models have been extensively used and enhanced in the surveillance domain because of their ability to adaptively describe multimodal distributions in real-time with low memory requirements. Nevertheless, they still often suffer from the problem of converging to poor solutions if the main mode stretches and thus over-dominates weaker distributions. Based on the results of the Split and Merge EM algorithm, in this paper we propose a solution to this problem. Therefore, we define an appropriate splitting operation and the corresponding criterion for the selection of candidate modes, for the case of background subtraction. The proposed method achieves better background models than state-of-the-art approaches and is low demanding in terms of processing time and memory requirements, therefore making it especially appealing in the surveillance domain.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121067841","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}
引用次数: 52
Robust Foreground and Abandonment Analysis for Large-Scale Abandoned Object Detection in Complex Surveillance Videos 复杂监控视频中大规模废弃目标检测的鲁棒前景与丢弃分析
2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.62
Quanfu Fan, Sharath Pankanti
{"title":"Robust Foreground and Abandonment Analysis for Large-Scale Abandoned Object Detection in Complex Surveillance Videos","authors":"Quanfu Fan, Sharath Pankanti","doi":"10.1109/AVSS.2012.62","DOIUrl":"https://doi.org/10.1109/AVSS.2012.62","url":null,"abstract":"We present a robust system for large-scale abandoned object detection (AOD) with low false positive rates and good detection accuracy under complex realistic scenarios. The robustness of our system is largely attributed to an approach we develop for foreground analysis, which can effectively differentiate foreground objects from background under challenging conditions such as lighting changes, low textureness and low contrast as well as cluttered background. This significantly eliminates false positives caused by lighting changes while retaining true drops better. We further perform abandonment analysis to reduce more false positives including those related to people, at a small cost of accuracy (≤ 2%). We demonstrate the effectiveness of our approach on two large data sets collected in various challenging scenes, providing detailed analysis of experiments.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126189200","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}
引用次数: 19
Combining Infrared and Visible Images Using Novel Transform and Statistical Information 利用新变换和统计信息结合红外和可见光图像
2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.24
Jeongmin Bae, Bonhwa Ku, D. Han, Hanseok Ko
{"title":"Combining Infrared and Visible Images Using Novel Transform and Statistical Information","authors":"Jeongmin Bae, Bonhwa Ku, D. Han, Hanseok Ko","doi":"10.1109/AVSS.2012.24","DOIUrl":"https://doi.org/10.1109/AVSS.2012.24","url":null,"abstract":"This paper proposes a novel combining method of infrared (IR) and visible images based on a Discrete Wavelet Frame (DWF) approach. In contrast to existing methods, IR image is transformed first using statistical information of the visible image to emphasize relevant information. In a multi-scale domain, we then assign appropriate weights to each pixel of sub-band approximation images through pixel level weighted average for emphasizing relevant information of the IR image while keeping texture information of the visible image. Representative experiments show that the proposed method outperforms exiting methods in image quality.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126735748","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}
引用次数: 4
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