2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)最新文献

Measuring Energy Expenditure in Sports by Thermal Video Analysis 用热视频分析测量运动中的能量消耗

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-08-24 DOI: 10.1109/CVPRW.2017.29

Rikke Gade, R. Larsen, T. Moeslund

引用次数: 2

Generating 5D Light Fields in Scattering Media for Representing 3D Images 在散射介质中产生5D光场以表示三维图像

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.169

E. Yuasa, Fumihiko Sakaue, J. Sato

引用次数: 0

Trusting the Computer in Computer Vision: A Privacy-Affirming Framework 在计算机视觉中信任计算机:一个隐私确认框架

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.178

A. Chen, M. Biglari-Abhari, K. Wang

{"title":"Trusting the Computer in Computer Vision: A Privacy-Affirming Framework","authors":"A. Chen, M. Biglari-Abhari, K. Wang","doi":"10.1109/CVPRW.2017.178","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.178","url":null,"abstract":"The use of surveillance cameras continues to increase, ranging from conventional applications such as law enforcement to newer scenarios with looser requirements such as gathering business intelligence. Humans still play an integral part in using and interpreting the footage from these systems, but are also a significant factor in causing unintentional privacy breaches. As computer vision methods continue to improve, we argue in this position paper that system designers should reconsider the role of machines in surveillance, and how automation can be used to help protect privacy. We explore this by discussing the impact of the human-in-the-loop, the potential for using abstraction and distributed computing to further privacy goals, and an approach for determining when video footage should be hidden from human users. We propose that in an ideal surveillance scenario, a privacy-affirming framework causes collected camera footage to be processed by computers directly, and never shown to humans. This implicitly requires humans to establish trust, to believe that computer vision systems can generate sufficiently accurate results without human supervision, so that if information about people must be gathered, unintentional data collection is mitigated as much as possible.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"65 1","pages":"1360-1367"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74401892","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: 11

I Know That Person: Generative Full Body and Face De-identification of People in Images 我认识那个人:图像中人的生成全身和面部去认同

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.173

K. Brkić, I. Sikirić, T. Hrkać, Z. Kalafatić

引用次数: 77

Real-Time Hand Grasp Recognition Using Weakly Supervised Two-Stage Convolutional Neural Networks for Understanding Manipulation Actions 基于弱监督两阶段卷积神经网络的实时手抓识别

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.67

Ji Woong Kim, Sujeong You, S. Ji, Hong-Seok Kim

引用次数: 2

What Will I Do Next? The Intention from Motion Experiment 接下来我该做什么?动作意图实验

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.7

Andrea Zunino, Jacopo Cavazza, A. Koul, A. Cavallo, C. Becchio, Vittorio Murino

{"title":"What Will I Do Next? The Intention from Motion Experiment","authors":"Andrea Zunino, Jacopo Cavazza, A. Koul, A. Cavallo, C. Becchio, Vittorio Murino","doi":"10.1109/CVPRW.2017.7","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.7","url":null,"abstract":"In computer vision, video-based approaches have been widely explored for the early classification and the prediction of actions or activities. However, it remains unclear whether this modality (as compared to 3D kinematics) can still be reliable for the prediction of human intentions, defined as the overarching goal embedded in an action sequence. Since the same action can be performed with different intentions, this problem is more challenging but yet affordable as proved by quantitative cognitive studies which exploit the 3D kinematics acquired through motion capture systems.In this paper, we bridge cognitive and computer vision studies, by demonstrating the effectiveness of video-based approaches for the prediction of human intentions. Precisely, we propose Intention from Motion, a new paradigm where, without using any contextual information, we consider instantaneous grasping motor acts involving a bottle in order to forecast why the bottle itself has been reached (to pass it or to place in a box, or to pour or to drink the liquid inside).We process only the grasping onsets casting intention prediction as a classification framework. Leveraging on our multimodal acquisition (3D motion capture data and 2D optical videos), we compare the most commonly used 3D descriptors from cognitive studies with state-of-the-art video-based techniques. Since the two analyses achieve an equivalent performance, we demonstrate that computer vision tools are effective in capturing the kinematics and facing the cognitive problem of human intention prediction.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"19 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79219674","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

Video Action Recognition Based on Deeper Convolution Networks with Pair-Wise Frame Motion Concatenation 基于对帧运动串联的深度卷积网络视频动作识别

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.162

Yamin Han, Peng Zhang, Tao Zhuo, Wei Huang, Yanning Zhang

{"title":"Video Action Recognition Based on Deeper Convolution Networks with Pair-Wise Frame Motion Concatenation","authors":"Yamin Han, Peng Zhang, Tao Zhuo, Wei Huang, Yanning Zhang","doi":"10.1109/CVPRW.2017.162","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.162","url":null,"abstract":"Deep convolution networks based strategies have shown a remarkable performance in different recognition tasks. Unfortunately, in a variety of realistic scenarios, accurate and robust recognition is hard especially for the videos. Different challenges such as cluttered backgrounds or viewpoint change etc. may generate the problem like large intrinsic and extrinsic class variations. In addition, the problem of data deficiency could also make the designed model degrade during learning and update. Therefore, an effective way by incorporating the frame-wise motion into the learning model on-the-fly has become more and more attractive in contemporary video analysis studies.,,,,,,To overcome those limitations, in this work, we proposed a deeper convolution networks based approach with pairwise motion concatenation, which is named deep temporal convolutional networks. In this work, a temporal motion accumulation mechanism has been introduced as an effective data entry for the learning of convolution networks. Specifically, to handle the possible data deficiency, beneficial practices of transferring ResNet-101 weights and data variation augmentation are also utilized for the purpose of robust recognition. Experiments on challenging dataset UCF101 and ODAR dataset have verified a preferable performance when compared with other state-of-art works.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"64 1","pages":"1226-1235"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91235967","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: 8

DeepSpace: Mood-Based Image Texture Generation for Virtual Reality from Music DeepSpace:基于情绪的音乐虚拟现实图像纹理生成

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.283

Misha Sra, Prashanth Vijayaraghavan, Ognjen Rudovic, P. Maes, D. Roy

引用次数: 5

Estimation of Affective Level in the Wild with Multiple Memory Networks 基于多记忆网络的野外情感水平估计

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.244

Jianshu Li, Yunpeng Chen, Shengtao Xiao, Jian Zhao, S. Roy, Jiashi Feng, Shuicheng Yan, T. Sim

引用次数: 15

Rear-Stitched View Panorama: A Low-Power Embedded Implementation for Smart Rear-View Mirrors on Vehicles 后缝全景视图:车辆智能后视镜的低功耗嵌入式实现

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Pub Date : 2017-07-21 DOI: 10.1109/CVPRW.2017.157

Janice Pan, Vikram V. Appia, Jesse Villarreal, Lucas Weaver, Do-Kyoung Kwon

{"title":"Rear-Stitched View Panorama: A Low-Power Embedded Implementation for Smart Rear-View Mirrors on Vehicles","authors":"Janice Pan, Vikram V. Appia, Jesse Villarreal, Lucas Weaver, Do-Kyoung Kwon","doi":"10.1109/CVPRW.2017.157","DOIUrl":"https://doi.org/10.1109/CVPRW.2017.157","url":null,"abstract":"Automobiles are currently equipped with a three-mirror system for rear-view visualization. The two side-view mirrors show close the periphery on the left and right sides of the vehicle, and the center rear-view mirror is typically adjusted to allow the driver to see through the vehicle's rear windshield. This three-mirror system, however, imposes safety concerns in requiring drivers to shift their attention and gaze to look in each mirror to obtain a full visualization of the rear-view surroundings, which takes attention off the scene in front of the vehicle. We present an alternative to the three-mirror rear-view system, which we call Rear-Stitched View Panorama (RSVP). The proposed system uses four rear-facing cameras, strategically placed to overcome the traditional blind spot problem, and stitches the feeds from each camera together to generate a single panoramic view, which can display the entire rear surroundings. We project individually captured frames onto a single virtual view using precomputed system calibration parameters. Then we determine optimal seam lines, along which the images are fused together to form the single RSVP view presented to the driver. Furthermore, we highlight techniques that enable efficient embedded implementation of the system and showcase a real-time system utilizing under 2W of power, making it suitable for in-cabin deployment in vehicles.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"32 1","pages":"1184-1193"},"PeriodicalIF":0.0,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81517148","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: 8