2017 IEEE International Conference on Computer Vision Workshops (ICCVW)最新文献

UCT: Learning Unified Convolutional Networks for Real-Time Visual Tracking UCT:学习统一卷积网络用于实时视觉跟踪

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-11-10 DOI: 10.1109/ICCVW.2017.231

Zheng Zhu, Guan Huang, Wei Zou, Dalong Du, Chang Huang

{"title":"UCT: Learning Unified Convolutional Networks for Real-Time Visual Tracking","authors":"Zheng Zhu, Guan Huang, Wei Zou, Dalong Du, Chang Huang","doi":"10.1109/ICCVW.2017.231","DOIUrl":"https://doi.org/10.1109/ICCVW.2017.231","url":null,"abstract":"Convolutional neural networks (CNN) based tracking approaches have shown favorable performance in recent benchmarks. Nonetheless, the chosen CNN features are always pre-trained in different task and individual components in tracking systems are learned separately, thus the achieved tracking performance may be suboptimal. Besides, most of these trackers are not designed towards realtime applications because of their time-consuming feature extraction and complex optimization details. In this paper, we propose an end-to-end framework to learn the convolutional features and perform the tracking process simultaneously, namely, a unified convolutional tracker (UCT). Specifically, The UCT treats feature extractor and tracking process (ridge regression) both as convolution operation and trains them jointly, enabling learned CNN features are tightly coupled to tracking process. In online tracking, an efficient updating method is proposed by introducing peak-versus-noise ratio (PNR) criterion, and scale changes are handled efficiently by incorporating a scale branch into network. The proposed approach results in superior tracking performance, while maintaining real-time speed. The standard UCT and UCT-Lite can track generic objects at 41 FPS and 154 FPS without further optimization, respectively. Experiments are performed on four challenging benchmark tracking datasets: OTB2013, OTB2015, VOT2014 and VOT2015, and our method achieves state-of-the-art results on these benchmarks compared with other real-time trackers.","PeriodicalId":149766,"journal":{"name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132276624","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: 77

Ancient Roman Coin Recognition in the Wild Using Deep Learning Based Recognition of Artistically Depicted Face Profiles 使用基于艺术描绘的面部轮廓识别的深度学习在野外识别古罗马硬币

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-29 DOI: 10.1109/ICCVW.2017.342

Imanol Schlag, Ognjen Arandjelovic

{"title":"Ancient Roman Coin Recognition in the Wild Using Deep Learning Based Recognition of Artistically Depicted Face Profiles","authors":"Imanol Schlag, Ognjen Arandjelovic","doi":"10.1109/ICCVW.2017.342","DOIUrl":"https://doi.org/10.1109/ICCVW.2017.342","url":null,"abstract":"As a particularly interesting application in the realm of cultural heritage on the one hand, and a technically challenging problem, computer vision based analysis of Roman Imperial coins has been attracting an increasing amount of research. In this paper we make several important contributions. Firstly, we address a key limitation of existing work which is largely characterized by the application of generic object recognition techniques and the lack of use of domain knowledge. In contrast, our work approaches coin recognition in much the same way as a human expert would: by identifying the emperor universally shown on the obverse. To this end we develop a deep convolutional network, carefully crafted for what is effectively a specific instance of profile face recognition. No less importantly, we also address a major methodological flaw of previous research which is, as we explain in detail, insufficiently systematic and rigorous, and mired with confounding factors. Lastly, we introduce three carefully collected and annotated data sets, and using these demonstrate the effectiveness of the proposed approach which is shown to exceed the performance of the state of the art by approximately an order of magnitude.","PeriodicalId":149766,"journal":{"name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128136182","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: 30

Particle Filter Based Probabilistic Forced Alignment for Continuous Gesture Recognition 基于粒子滤波的连续手势识别概率强制对齐

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-29 DOI: 10.1109/ICCVW.2017.364

Necati Cihan Camgöz, Simon Hadfield, R. Bowden

{"title":"Particle Filter Based Probabilistic Forced Alignment for Continuous Gesture Recognition","authors":"Necati Cihan Camgöz, Simon Hadfield, R. Bowden","doi":"10.1109/ICCVW.2017.364","DOIUrl":"https://doi.org/10.1109/ICCVW.2017.364","url":null,"abstract":"In this paper, we propose a novel particle filter based probabilistic forced alignment approach for training spatiotemporal deep neural networks using weak border level annotations. The proposed method jointly learns to localize and recognize isolated instances in continuous streams. This is done by drawing training volumes from a prior distribution of likely regions and training a discriminative 3D-CNN from this data. The classifier is then used to calculate the posterior distribution by scoring the training examples and using this as the prior for the next sampling stage. We apply the proposed approach to the challenging task of large-scale user-independent continuous gesture recognition. We evaluate the performance on the popular ChaLearn 2016 Continuous Gesture Recognition (ConGD) dataset. Our method surpasses state-of-the-art results by obtaining 0.3646 and 0.3744 Mean Jaccard Index Score on the validation and test sets of ConGD, respectively. Furthermore, we participated in the ChaLearn 2017 Continuous Gesture Recognition Challenge and was ranked 3rd. It should be noted that our method is learner independent, it can be easily combined with other approaches.","PeriodicalId":149766,"journal":{"name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125991351","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

Propagation of Orientation Uncertainty of 3D Rigid Object to Its Points 三维刚体方向不确定性向其点的传播

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-29 DOI: 10.1109/ICCVW.2017.255

M. Franaszek, G. Cheok

引用次数: 2

BEHAVE — Behavioral Analysis of Visual Events for Assisted Living Scenarios 辅助生活场景中视觉事件的行为分析

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-28 DOI: 10.1109/ICCVW.2017.160

Jonas Vlasselaer, C. Crispim, F. Brémond, Anton Dries

引用次数: 1

Spatially-Variant Kernel for Optical Flow Under Low Signal-to-Noise Ratios Application to Microscopy 低信噪比下光流空间变核在显微镜中的应用

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-23 DOI: 10.1109/ICCVW.2017.12

Denis Fortun, N. Debroux, C. Kervrann

{"title":"Spatially-Variant Kernel for Optical Flow Under Low Signal-to-Noise Ratios Application to Microscopy","authors":"Denis Fortun, N. Debroux, C. Kervrann","doi":"10.1109/ICCVW.2017.12","DOIUrl":"https://doi.org/10.1109/ICCVW.2017.12","url":null,"abstract":"Local and global approaches can be identified as the two main classes of optical flow estimation methods. In this paper, we propose a framework to combine the advantages of these two principles, namely robustness to noise of the local approach and discontinuity preservation of the global approach. This is particularly crucial in biological imaging, where the noise produced by microscopes is one of the main issues for optical flow estimation. The idea is to adapt spatially the local support of the local parametric constraint in the combined local-global model [6]. To this end, we jointly estimate the motion field and the parameters of the spatial support. We apply our approach to the case of Gaussian filtering, and we derive efficient minimization schemes for usual data terms. The estimation of a spatially varying standard deviation map prevents from the smoothing of motion discontinuities, while ensuring robustness to noise. We validate our method for a standard model and demonstrate how a baseline approach with pixel-wise data term can be improved when integrated in our framework. The method is evaluated on the Middlebury benchmark with ground truth and on real fluorescence microscopy data.","PeriodicalId":149766,"journal":{"name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123657543","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: 0

Human Action Recognition: Pose-Based Attention Draws Focus to Hands 人类动作识别:基于姿势的注意力将焦点吸引到手上

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-23 DOI: 10.1109/ICCVW.2017.77

Fabien Baradel, Christian Wolf, J. Mille

引用次数: 91

Exploiting the Complementarity of Audio and Visual Data in Multi-speaker Tracking 在多说话人跟踪中利用视听数据的互补性

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-23 DOI: 10.1109/ICCVW.2017.60

Yutong Ban, Laurent Girin, Xavier Alameda-Pineda, R. Horaud

引用次数: 22

Registration of RGB and Thermal Point Clouds Generated by Structure From Motion 由运动结构生成的RGB和热点云的配准

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-22 DOI: 10.1109/ICCVW.2017.57

Trong Phuc Truong, M. Yamaguchi, Shohei Mori, Vincent Nozick, H. Saito

引用次数: 22

A Handcrafted Normalized-Convolution Network for Texture Classification 纹理分类的手工归一化卷积网络

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-22 DOI: 10.1109/ICCVW.2017.149

Ngoc-Son Vu, Vu-Lam Nguyen, P. Gosselin

引用次数: 6