IPSJ Transactions on Computer Vision and Applications最新文献_第8页

Upper Body Pose Estimation for Team Sports Videos Using a Poselet-Regressor of Spine Pose and Body Orientation Classifiers Conditioned by the Spine Angle Prior 基于脊柱姿态和姿态分类器的团队运动视频上半身姿态估计

IPSJ Transactions on Computer Vision and Applications Pub Date : 2015-10-20 DOI: 10.2197/ipsjtcva.7.121

Masaki Hayashi, Kyoko Oshima, Masamoto Tanabiki, Y. Aoki

{"title":"Upper Body Pose Estimation for Team Sports Videos Using a Poselet-Regressor of Spine Pose and Body Orientation Classifiers Conditioned by the Spine Angle Prior","authors":"Masaki Hayashi, Kyoko Oshima, Masamoto Tanabiki, Y. Aoki","doi":"10.2197/ipsjtcva.7.121","DOIUrl":"https://doi.org/10.2197/ipsjtcva.7.121","url":null,"abstract":"We propose a per-frame upper body pose estimation method for sports players captured in low-resolution team sports videos. Using the head-center-aligned upper body region appearance in each frame from the head tracker, our framework estimates (1) 2D spine pose, composed of the head center and the pelvis center locations, and (2) the orientation of the upper body in each frame. Our framework is composed of three steps. In the first step, the head region of the subject player is tracked with a standard tracking-by-detection technique for upper body appearance alignment. In the second step, the relative pelvis center location from the head center is estimated by our newly proposed poseletregressor in each frame to obtain spine angle priors. In the last step, the body orientation is estimated by the upper body orientation classifier selected by the spine angle range. Owing to the alignment of the body appearance and the usage of multiple body orientation classifiers conditioned by the spine angle prior, our method can robustly estimate the body orientation of a player with a large variation of visual appearances during a game, even during side-poses or self-occluded poses. We tested the performance of our method in both American football and soccer videos.","PeriodicalId":38957,"journal":{"name":"IPSJ Transactions on Computer Vision and Applications","volume":"37 1","pages":"121-137"},"PeriodicalIF":0.0,"publicationDate":"2015-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84868702","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

Human Behavior Recognition in Shopping Settings 购物环境中的人类行为识别

IPSJ Transactions on Computer Vision and Applications Pub Date : 2015-01-01 DOI: 10.2197/ipsjtcva.7.151

R. Sicre, H. Nicolas

引用次数: 1

A General Inlier Estimation for Moving Camera Motion Segmentation 运动摄像机运动分割的一般插值估计

IPSJ Transactions on Computer Vision and Applications Pub Date : 2015-01-01 DOI: 10.2197/ipsjtcva.7.163

Xuefeng Liang, Cuicui Zhang, T. Matsuyama

{"title":"A General Inlier Estimation for Moving Camera Motion Segmentation","authors":"Xuefeng Liang, Cuicui Zhang, T. Matsuyama","doi":"10.2197/ipsjtcva.7.163","DOIUrl":"https://doi.org/10.2197/ipsjtcva.7.163","url":null,"abstract":"In moving camera videos, motion segmentation is often achieved by determining the motion coherence of each moving object. However, it is a nontrivial task on optical flow due to two problems: 1) Optical flow of the camera motions in 3D world consists of three primary 2D motion flows: translation, rotation, and radial flow. Their coherence analysis is done by a variety of models, and further requires plenty of priors in existing frameworks; 2) A moving camera introduces 3D motion, the depth discontinuities cause the motion discontinuities that severely break down the coherence. Meanwhile, the mixture of the camera motion and moving objects’ motions make it difficult to clearly identify foreground and background. In this work, our solution is to transform the optical flow into a potential space where the coherence of the background flow field is easily modeled by a low order polynomial. To this end, we first amend the Helmholts-Hodge Decomposition by adding coherence constraints, which can transform translation, rotation, and radial flow fields to two potential surfaces under a unified framework. Secondly, we introduce an Incoherence Map and a progressive Quad-Tree partition to reject moving objects and motion discontinuities. Finally, the low order polynomial is achieved from the rest flow samples on two potentials. We present results on more than twenty videos from four benchmarks. Extensive experiments demonstrate better performance in dealing with challenging scenes with complex backgrounds. Our method improves the segmentation accuracy of state-of-the-arts by 10%∼30%.","PeriodicalId":38957,"journal":{"name":"IPSJ Transactions on Computer Vision and Applications","volume":"33 1","pages":"163-174"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86286320","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: 3

Spatial Visual Attention for Novelty Detection: A Space-based Saliency Model in 3D Using Spatial Memory 新颖性检测的空间视觉注意:基于空间记忆的三维空间显著性模型

IPSJ Transactions on Computer Vision and Applications Pub Date : 2015-01-01 DOI: 10.2197/ipsjtcva.7.35

Nevrez Imamoglu, E. Dorronzoro, M. Sekine, K. Kita, Wenwei Yu

{"title":"Spatial Visual Attention for Novelty Detection: A Space-based Saliency Model in 3D Using Spatial Memory","authors":"Nevrez Imamoglu, E. Dorronzoro, M. Sekine, K. Kita, Wenwei Yu","doi":"10.2197/ipsjtcva.7.35","DOIUrl":"https://doi.org/10.2197/ipsjtcva.7.35","url":null,"abstract":"Saliency maps as visual attention computational models can reveal novel regions within a scene (as in the human visual system), which can decrease the amount of data to be processed in task specific computer vision applications. Most of the saliency computation models do not take advantage of prior spatial memory by giving priority to spatial or object based features to obtain bottom-up or top-down saliency maps. In our previous experiments, we demonstrated that spatial memory regardless of object features can aid detection and tracking tasks with a mobile robot by using a 2D global environment memory of the robot and local Kinect data in 2D to compute the space-based saliency map. However, in complex scenes where 2D space-based saliency is not enough (i.e., subject lying on the bed), 3D scene analysis is necessary to extract novelty within the scene by using spatial memory. Therefore, in this work, to improve the detection of novelty in a known environment, we proposed a space-based spatial saliency with 3D local information by improving 2D space base saliency with height as prior information about the specific locations. Moreover, the algorithm can also be integrated with other bottom-up or top-down saliency computational models to improve the detection results. Experimental results demonstrate that high accuracy for novelty detection can be obtained, and computational time can be reduced for existing state of the art detection and tracking models with the proposed algorithm.","PeriodicalId":38957,"journal":{"name":"IPSJ Transactions on Computer Vision and Applications","volume":"158 1","pages":"35-40"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88859146","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: 3

Audio-Visual Speech Recognition Using Convolutive Bottleneck Networks for a Person with Severe Hearing Loss 基于卷积瓶颈网络的重度听力损失人的视听语音识别

IPSJ Transactions on Computer Vision and Applications Pub Date : 2015-01-01 DOI: 10.2197/ipsjtcva.7.64

Yuki Takashima, Yasuhiro Kakihara, Ryo Aihara, T. Takiguchi, Y. Ariki, Nobuyuki Mitani, K. Omori, Kaoru Nakazono

引用次数: 5

Rail Sensor: A Mobile Lidar System for 3D Archiving the Bas-reliefs in Angkor Wat 轨道传感器:用于吴哥窟浅浮雕3D存档的移动激光雷达系统

IPSJ Transactions on Computer Vision and Applications Pub Date : 2015-01-01 DOI: 10.2197/ipsjtcva.7.59

Bo Zheng, Takeshi Oishi, K. Ikeuchi

引用次数: 11

Image Denoising with Sparsity Distillation 稀疏度蒸馏图像去噪

IPSJ Transactions on Computer Vision and Applications Pub Date : 2015-01-01 DOI: 10.2197/ipsjtcva.7.50

S. Kawata, Nao Mishima

引用次数: 1

Auxiliary Training Information Assisted Visual Recognition 辅助训练信息辅助视觉识别

IPSJ Transactions on Computer Vision and Applications Pub Date : 2015-01-01 DOI: 10.2197/ipsjtcva.7.138

Qilin Zhang, G. Hua, W. Liu, Zicheng Liu, Zhengyou Zhang

{"title":"Auxiliary Training Information Assisted Visual Recognition","authors":"Qilin Zhang, G. Hua, W. Liu, Zicheng Liu, Zhengyou Zhang","doi":"10.2197/ipsjtcva.7.138","DOIUrl":"https://doi.org/10.2197/ipsjtcva.7.138","url":null,"abstract":"In the realm of multi-modal visual recognition, the reliability of the data acquisition system is often a concern due to the increased complexity of the sensors. One of the major issues is the accidental loss of one or more sensing channels, which poses a major challenge to current learning systems. In this paper, we examine one of these specific missing data problems, where we have a main modality/view along with an auxiliary modality/view present in the training data, but merely the main modality/view in the test data. To effectively leverage the auxiliary information to train a stronger classifier, we propose a collaborative auxiliary learning framework based on a new discriminative canonical correlation analysis. This framework reveals a common semantic space shared across both modalities/views through enforcing a series of nonlinear projections. Such projections automatically embed the discriminative cues hidden in both modalities/views into the common space, and better visual recognition is thus achieved on the test data. The efficacy of our proposed auxiliary learning approach is demonstrated through four challenging visual recognition tasks with different kinds of auxiliary information.","PeriodicalId":38957,"journal":{"name":"IPSJ Transactions on Computer Vision and Applications","volume":"75 1","pages":"138-150"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79495291","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

Depth-based Gait Authentication for Practical Sensor Settings 基于深度的实际传感器设置步态认证

IPSJ Transactions on Computer Vision and Applications Pub Date : 2015-01-01 DOI: 10.2197/ipsjtcva.7.94

Taro Ikeda, Ikuhisa Mitsugami, Y. Yagi

引用次数: 3

Individuality-preserving Silhouette Extraction for Gait Recognition 步态识别中保持个性的轮廓提取

IPSJ Transactions on Computer Vision and Applications Pub Date : 2015-01-01 DOI: 10.2197/ipsjtcva.7.74

Yasushi Makihara, Takuya Tanoue, D. Muramatsu, Y. Yagi, Syunsuke Mori, Yuzuko Utsumi, M. Iwamura, K. Kise

{"title":"Individuality-preserving Silhouette Extraction for Gait Recognition","authors":"Yasushi Makihara, Takuya Tanoue, D. Muramatsu, Y. Yagi, Syunsuke Mori, Yuzuko Utsumi, M. Iwamura, K. Kise","doi":"10.2197/ipsjtcva.7.74","DOIUrl":"https://doi.org/10.2197/ipsjtcva.7.74","url":null,"abstract":"Most gait recognition approaches rely on silhouette-based representations due to high recognition accu- racy and computational efficiency, and a key problem for those approaches is how to accurately extract individuality- preserved silhouettes from real scenes, where foreground colors may be similar to background colors and the back- groundis cluttered. We thereforeproposea method of individuality-preservingsilhouetteextractionfor gait recognition using standard gait models (SGMs) composed of clean silhouette sequences of a variety of training subjects as a shape prior. We firstly match the multiple SGMs to a background subtraction sequence of a test subject by dynamic pro- gramming and select the training subject whose SGM fit the test sequence the best. We then formulate our silhouette extraction problem in a well-established graph-cut segmentation framework while considering a balance between the observed test sequence and the matched SGM. More specifically, we define an energy function to be minimized by the following three terms: (1) a data term derived from the observed test sequence, (2) a smoothness term derived from spatio-temporally adjacent edges, and (3) a shape-prior term derived from the matched SGM. We demonstrate that the proposed method successfully extracts individuality-preserved silhouettes and improved gait recognition accuracy through experiments using 56 subjects.","PeriodicalId":38957,"journal":{"name":"IPSJ Transactions on Computer Vision and Applications","volume":"20 1","pages":"74-78"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88202496","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: 16