2017 IEEE International Conference on Computer Vision Workshops (ICCVW)最新文献_第2页

Visual Tracking of Small Animals in Cluttered Natural Environments Using a Freely Moving Camera 在杂乱的自然环境中使用自由移动相机的小动物视觉跟踪

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-22 DOI: 10.1109/ICCVW.2017.335

B. Risse, M. Mangan, B. Webb, Luca Del Pero

{"title":"Visual Tracking of Small Animals in Cluttered Natural Environments Using a Freely Moving Camera","authors":"B. Risse, M. Mangan, B. Webb, Luca Del Pero","doi":"10.1109/ICCVW.2017.335","DOIUrl":"https://doi.org/10.1109/ICCVW.2017.335","url":null,"abstract":"Image-based tracking of animals in their natural habitats can provide rich behavioural data, but is very challenging due to complex and dynamic background and target appearances. We present an effective method to recover the positions of terrestrial animals in cluttered environments from video sequences filmed using a freely moving monocular camera. The method uses residual motion cues to detect the targets and is thus robust to different lighting conditions and requires no a-priori appearance model of the animal or environment. The detection is globally optimised based on an inference problem formulation using factor graphs. This handles ambiguities such as occlusions and intersections and provides automatic initialisation. Furthermore, this formulation allows a seamless integration of occasional user input for the most difficult situations, so that the effect of a few manual position estimates are smoothly distributed over long sequences. Testing our system against a benchmark dataset featuring small targets in natural scenes, we obtain 96% accuracy for fully automated tracking. We also demonstrate reliable tracking in a new data set that includes different targets (insects, vertebrates or artificial objects) in a variety of environments (desert, jungle, meadows, urban) using different imaging devices (day / night vision cameras, smart phones) and modalities (stationary, hand-held, drone operated).","PeriodicalId":149766,"journal":{"name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114726560","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: 33

SnapNet-R: Consistent 3D Multi-view Semantic Labeling for Robotics SnapNet-R:用于机器人的一致3D多视图语义标记

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-22 DOI: 10.1109/ICCVW.2017.85

J. Guerry, Alexandre Boulch, B. L. Saux, J. Moras, A. Plyer, David Filliat

引用次数: 62

Fully Convolutional Network and Region Proposal for Instance Identification with Egocentric Vision 自我中心视觉实例识别的全卷积网络和区域建议

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-22 DOI: 10.1109/ICCVW.2017.281

Maxime Portaz, Matthias Kohl, G. Quénot, J. Chevallet

引用次数: 13

Results and Analysis of ChaLearn LAP Multi-modal Isolated and Continuous Gesture Recognition, and Real Versus Fake Expressed Emotions Challenges ChaLearn LAP多模态孤立和连续手势识别的结果与分析，以及真实与虚假表达情绪的挑战

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-22 DOI: 10.1109/ICCVW.2017.377

Jun Wan, Sergio Escalera, G. Anbarjafari, H. Escalante, Xavier Baró, Isabelle M Guyon, Meysam Madadi, J. Allik, Jelena Gorbova, Chi Lin, Yiliang Xie

{"title":"Results and Analysis of ChaLearn LAP Multi-modal Isolated and Continuous Gesture Recognition, and Real Versus Fake Expressed Emotions Challenges","authors":"Jun Wan, Sergio Escalera, G. Anbarjafari, H. Escalante, Xavier Baró, Isabelle M Guyon, Meysam Madadi, J. Allik, Jelena Gorbova, Chi Lin, Yiliang Xie","doi":"10.1109/ICCVW.2017.377","DOIUrl":"https://doi.org/10.1109/ICCVW.2017.377","url":null,"abstract":"We analyze the results of the 2017 ChaLearn Looking at People Challenge at ICCV The challenge comprised three tracks: (1) large-scale isolated (2) continuous gesture recognition, and (3) real versus fake expressed emotions tracks. It is the second round for both gesture recognition challenges, which were held first in the context of the ICPR 2016 workshop on \"multimedia challenges beyond visual analysis\". In this second round, more participants joined the competitions, and the performances considerably improved compared to the first round. Particularly, the best recognition accuracy of isolated gesture recognition has improved from 56.90% to 67.71% in the IsoGD test set, and Mean Jaccard Index (MJI) of continuous gesture recognition has improved from 0.2869 to 0.6103 in the ConGD test set. The third track is the first challenge on real versus fake expressed emotion classification, including six emotion categories, for which a novel database was introduced. The first place was shared between two teams who achieved 67.70% averaged recognition rate on the test set. The data of the three tracks, the participants' code and method descriptions are publicly available to allow researchers to keep making progress in the field.","PeriodicalId":149766,"journal":{"name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","volume":"330 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134263313","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: 72

Visualizing Apparent Personality Analysis with Deep Residual Networks 用深度残差网络可视化表观人格分析

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-22 DOI: 10.1109/ICCVW.2017.367

Yağmur Güçlütürk, Umut Güçlü, Marc Pérez, H. Escalante, Xavier Baró, C. Andújar, Isabelle M Guyon, Julio C. S. Jacques Junior, Meysam Madadi, Sergio Escalera, M. Gerven, R. Lier

{"title":"Visualizing Apparent Personality Analysis with Deep Residual Networks","authors":"Yağmur Güçlütürk, Umut Güçlü, Marc Pérez, H. Escalante, Xavier Baró, C. Andújar, Isabelle M Guyon, Julio C. S. Jacques Junior, Meysam Madadi, Sergio Escalera, M. Gerven, R. Lier","doi":"10.1109/ICCVW.2017.367","DOIUrl":"https://doi.org/10.1109/ICCVW.2017.367","url":null,"abstract":"Automatic prediction of personality traits is a subjective task that has recently received much attention. Specifically, automatic apparent personality trait prediction from multimodal data has emerged as a hot topic within the filed of computer vision and, more particularly, the so called \"looking at people\" sub-field. Considering \"apparent\" personality traits as opposed to real ones considerably reduces the subjectivity of the task. The real world applications are encountered in a wide range of domains, including entertainment, health, human computer interaction, recruitment and security. Predictive models of personality traits are useful for individuals in many scenarios (e.g., preparing for job interviews, preparing for public speaking). However, these predictions in and of themselves might be deemed to be untrustworthy without human understandable supportive evidence. Through a series of experiments on a recently released benchmark dataset for automatic apparent personality trait prediction, this paper characterizes the audio and visual information that is used by a state-of-the-art model while making its predictions, so as to provide such supportive evidence by explaining predictions made. Additionally, the paper describes a new web application, which gives feedback on apparent personality traits of its users by combining model predictions with their explanations.","PeriodicalId":149766,"journal":{"name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127590574","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: 18

Adaptive Pooling in Multi-instance Learning for Web Video Annotation Web视频标注多实例学习中的自适应池

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-22 DOI: 10.1109/ICCVW.2017.46

Dong Liu, Y. Zhou, Xiaoyan Sun, Zhengjun Zha, Wenjun Zeng

{"title":"Adaptive Pooling in Multi-instance Learning for Web Video Annotation","authors":"Dong Liu, Y. Zhou, Xiaoyan Sun, Zhengjun Zha, Wenjun Zeng","doi":"10.1109/ICCVW.2017.46","DOIUrl":"https://doi.org/10.1109/ICCVW.2017.46","url":null,"abstract":"Web videos are usually weakly annotated, i.e., a tag is associated to a video once the corresponding concept appears in a frame of this video without indicating when and where it occurs. These weakly annotated tags pose big troubles to many Web video applications, e.g. search and recommendation. In this paper, we present a new Web video annotation approach based on multi-instance learning (MIL) with a learnable pooling function. By formulating the Web video annotation as a MIL problem, we present an end-to-end deep network framework to solve this problem in which the frame (instance) level annotation is estimated from tags given at the video (bag of instances) level via a convolutional neural network (CNN). A learnable pooling function is proposed to adaptively fuse the outputs of the CNN to determine tags at the video level. We further propose a new loss function that consists of both bag-level and instance-level losses, which enables the penalty term to be aware of the internal state of network rather than only an overall loss, thus makes the pooling function learned better and faster. Experimental results demonstrate that our proposed framework is able to not only enhance the accuracy of Web video annotation by outperforming the state-of-the-art Web video annotation methods on the large-scale video dataset FCVID, but also help to infer the most relevant frames in Web videos.","PeriodicalId":149766,"journal":{"name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127290844","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: 38

HyKo: A Spectral Dataset for Scene Understanding HyKo:场景理解的光谱数据集

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-01 DOI: 10.1109/ICCVW.2017.39

Christian Winkens, Florian Sattler, Veronika Adams, D. Paulus

引用次数: 3

Computer Vision Problems in Plant Phenotyping, CVPPP 2017: Introduction to the CVPPP 2017 Workshop Papers 植物表型中的计算机视觉问题，CVPPP 2017: CVPPP 2017研讨会论文介绍

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-01 DOI: 10.1109/ICCVW.2017.236

H. Scharr, T. Pridmore, S. Tsaftaris

引用次数: 17

Lightweight Monocular Obstacle Avoidance by Salient Feature Fusion 基于显著特征融合的轻型单目避障方法

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-01 DOI: 10.1109/ICCVW.2017.92

Andrea Manno-Kovács, Levente Kovács

引用次数: 0

Towards Implicit Correspondence in Signed Distance Field Evolution 符号距离场演化中的隐对应

2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Pub Date : 2017-10-01 DOI: 10.1109/ICCVW.2017.103

Miroslava Slavcheva, Maximilian Baust, Slobodan Ilic

引用次数: 10