2011 International Conference on Computer Vision最新文献

Spatio-temporal clustering of probabilistic region trajectories 概率区域轨迹的时空聚类

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126438

Fabio Galasso, M. Iwasaki, K. Nobori, R. Cipolla

{"title":"Spatio-temporal clustering of probabilistic region trajectories","authors":"Fabio Galasso, M. Iwasaki, K. Nobori, R. Cipolla","doi":"10.1109/ICCV.2011.6126438","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126438","url":null,"abstract":"We propose a novel model for the spatio-temporal clustering of trajectories based on motion, which applies to challenging street-view video sequences of pedestrians captured by a mobile camera. A key contribution of our work is the introduction of novel probabilistic region trajectories, motivated by the non-repeatability of segmentation of frames in a video sequence. Hierarchical image segments are obtained by using a state-of-the-art hierarchical segmentation algorithm, and connected from adjacent frames in a directed acyclic graph. The region trajectories and measures of confidence are extracted from this graph using a dynamic programming-based optimisation. Our second main contribution is a Bayesian framework with a twofold goal: to learn the optimal, in a maximum likelihood sense, Random Forests classifier of motion patterns based on video features, and construct a unique graph from region trajectories of different frames, lengths and hierarchical levels. Finally, we demonstrate the use of Isomap for effective spatio-temporal clustering of the region trajectories of pedestrians. We support our claims with experimental results on new and existing challenging video sequences.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73565378","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

Density-aware person detection and tracking in crowds 人群中具有密度意识的人员检测和跟踪

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126526

Mikel D. Rodriguez, I. Laptev, Josef Sivic, Jean-Yves Audibert

引用次数: 355

An adaptive coupled-layer visual model for robust visual tracking 一种鲁棒视觉跟踪的自适应耦合层视觉模型

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126390

Luka Cehovin, M. Kristan, A. Leonardis

引用次数: 107

Trajectory reconstruction from non-overlapping surveillance cameras with relative depth ordering constraints 基于相对深度排序约束的非重叠监控摄像机轨迹重建

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126334

B. Micusík

引用次数: 7

Video from a single coded exposure photograph using a learned over-complete dictionary 视频从一个单一的编码曝光照片使用学习过完整的字典

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126254

Y. Hitomi, Jinwei Gu, Mohit Gupta, T. Mitsunaga, S. Nayar

{"title":"Video from a single coded exposure photograph using a learned over-complete dictionary","authors":"Y. Hitomi, Jinwei Gu, Mohit Gupta, T. Mitsunaga, S. Nayar","doi":"10.1109/ICCV.2011.6126254","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126254","url":null,"abstract":"Cameras face a fundamental tradeoff between the spatial and temporal resolution - digital still cameras can capture images with high spatial resolution, but most high-speed video cameras suffer from low spatial resolution. It is hard to overcome this tradeoff without incurring a significant increase in hardware costs. In this paper, we propose techniques for sampling, representing and reconstructing the space-time volume in order to overcome this tradeoff. Our approach has two important distinctions compared to previous works: (1) we achieve sparse representation of videos by learning an over-complete dictionary on video patches, and (2) we adhere to practical constraints on sampling scheme which is imposed by architectures of present image sensor devices. Consequently, our sampling scheme can be implemented on image sensors by making a straightforward modification to the control unit. To demonstrate the power of our approach, we have implemented a prototype imaging system with per-pixel coded exposure control using a liquid crystal on silicon (LCoS) device. Using both simulations and experiments on a wide range of scenes, we show that our method can effectively reconstruct a video from a single image maintaining high spatial resolution.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80006886","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: 233

Random ensemble metrics for object recognition 用于对象识别的随机集成度量

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126466

Tatsuo Kozakaya, S. Ito, Susumu Kubota

{"title":"Random ensemble metrics for object recognition","authors":"Tatsuo Kozakaya, S. Ito, Susumu Kubota","doi":"10.1109/ICCV.2011.6126466","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126466","url":null,"abstract":"This paper presents a novel and generic approach for metric learning, random ensemble metrics (REMetric). To improve generalization performance, we introduce the concept of ensemble learning to the metric learning scheme. Unlike previous methods, our method does not optimize the global objective function for the whole training data. It learns multiple discriminative projection vectors obtained from linear support vector machines (SVM) using randomly subsampled training data. The final metric matrix is then obtained by integrating these vectors. As a result of using SVM, the learned metric has an excellent scalability for the dimensionality of features. Therefore, it does not require any prior dimensionality reduction techniques such as PCA. Moreover, our method allows us to unify dimensionality reduction and metric learning by controlling the number of the projection vectors. We demonstrate through experiments, that our method can avoid overfitting even though a relatively small number of training data is provided. The experiments are performed with three different datasets; the Viewpoint Invariant Pedestrian Recognition (VIPeR) dataset, the Labeled Face in the Wild (LFW) dataset and the Oxford 102 category flower dataset. The results show that our method achieves equivalent or superior performance compared to existing state-of-the-art metric learning methods.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80199053","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: 21

Isotonic CCA for sequence alignment and activity recognition 序列比对和活动识别的等渗CCA

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126545

Shahriar Shariat, V. Pavlovic

引用次数: 37

Robust unsupervised motion pattern inference from video and applications 鲁棒无监督运动模式推断视频和应用

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126308

Xuemei Zhao, G. Medioni

{"title":"Robust unsupervised motion pattern inference from video and applications","authors":"Xuemei Zhao, G. Medioni","doi":"10.1109/ICCV.2011.6126308","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126308","url":null,"abstract":"We propose an unsupervised learning framework to infer motion patterns in videos and in turn use them to improve tracking of moving objects in sequences from static cameras. Based on tracklets, we use a manifold learning method Tensor Voting to infer the local geometric structures in (x, y) space, and embed tracklet points into (x, y, θ) space, where θ represents motion direction. In this space, points automatically form intrinsic manifold structures, each of which corresponds to a motion pattern. To define each group, a novel robustmanifold grouping algorithm is proposed. Tensor Voting is performed to provide multiple geometric cues which formulate multiple similarity kernels between any pair of points, and a spectral clustering technique is used in this multiple kernel setting. The grouping algorithm achieves better performance than state-of-the-art methods in our applications. Extracted motion patterns can then be used as a prior to improve the performance of any object tracker. It is especially useful to reduce false alarms and ID switches. Experiments are performed on challenging real-world sequences, and a quantitative analysis of the results shows the framework effectively improves state-of-the-art tracker.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81533417","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: 31

Annotator rationales for visual recognition 用于视觉识别的注释器原理

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126394

Jeff Donahue, K. Grauman

引用次数: 76

Fisher Discrimination Dictionary Learning for sparse representation 稀疏表示的Fisher判别字典学习

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126286

Meng Yang, Lei Zhang, Xiangchu Feng, D. Zhang

引用次数: 972