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检索条件"任意字段=27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014"

共 227 条记录，以下是161-170 订阅

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Ground-Based Activity recognition at Distance and Behind Wall

Ground-Based Activity Recognition at Distance and Behind Wal...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wang, Tao Hammoud, Riad Zhu, Zhigang BAE Syst 6 New England Execut Pk Burlington MA 01803 USA CUNY Dept Comp Sci New York NY 10031 USA

ISBN: (纸本)9781479943098

Long-range activity recognition is a challenging research problem in a surveillance area where sensors cannot be placed close to targets. Even a simple activity can be confused with other activities or not be recognized correctly if the detection in one of the sensor modalities is not certain or even unavailable. Also, the training of some real-life activities is not feasible, because it is hard to collect sufficient and accurate labeled data for varieties of free-living activities. In this paper, we use an unsupervised learning algorithm, Dirichlet process Gaussian mixture model (DPGMM), to construct a model to determine the number of classes automatically. To further represent a set of features as one event, and communicate between both audio and video, we use the DPGMM as a base and enhance it with additional aggregation, multimodal association and transition. this new model is called aggregation coupled Dirichlet process Gaussian mixture model (AC-DPGMM). We present experiments with some activities that cannot be simply distinguished using visual features only. Along with audio information, we can also recognize some activities invisible in video, such as speaking behind a wall. We compared our model with a generative clustering algorithm and the original DPGMM, and showed that we have 23.6% and 18.8% improvement in accuracy compared with manually labeled data.

关键词： activity recognition audio-video long range sensing

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Towards Automated Understanding of Student-Tutor Interactions using Visual Deictic Gestures

Towards Automated Understanding of Student-Tutor Interaction...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Sathyanarayana, Suchitra Satzoda, Ravi Kumar Carini, Amber Lee, Monique Salamanca, Linda Reilly, Judy Forster, Deborah Bartlett, Marian Littlewort, Gwen Univ Calif San Diego La Jolla CA 92093 USA San Diego State Univ San Diego CA 92182 USA

ISBN: (纸本)9781479943098

In this paper, we present techniques for automated understanding of tutor-student behavior through detecting visual deictic gestures, in the context of one-to-one mathematics tutoring. To the best knowledge of the authors, this is the first work in the area of intelligent tutoring systems, which focuses on spatial localization of deictic gestural activity, i. e. where the deictic gesture is pointing on the workspace. A new dataset called SDMAth is first introduced. the motivation for detecting deictic gestures and their spatial properties is established, followed by techniques for automatic localization of deictic gestures in a workspace. the techniques employ computer vision and machine learning steps such as GBVS saliency, binary morphology and HOG-SVM classification. It is shown that the method localizes the deictic tip with an accuracy of over 85 % accuracy for a cut off distance of 12 pixels. Furthermore, a detailed discussion using examples from the proposed dataset is presented on high-level inferences about the student-tutor interactions that can be derived from the integration of spatial and temporal localization of the deictic gestural activity using the proposed techniques.

关键词： Gesture recognition

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A High-Performance Hardware Architecture for a Frameless Stereo vision Algorithm Implemented on a FPGA Platform

A High-Performance Hardware Architecture for a Frameless Ste...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Eibensteiner, Florian Kogler, Juergen Scharinger, Josef Upper Austria Univ Appl Sci Softwarepk 11 A-4232 Hagenberg Austria Austrian Inst Technol GmbH AIT A-1220 Vienna Austria Johannes Kepler Univ Linz A-4040 Linz Austria

ISBN: (纸本)9781479943098

As a novelty, in this paper we present an event-based stereo vision matching approach based on time-correlation using segmentation to restrict the matching process to active image areas, exploiting the event-driven behavior of a silicon retina sensor. Stereo matching is used in depth generating camera systems for solving the correspondence problem and reconstructing 3D data. Using conventionally frame-based cameras, this correspondence problem is a time consuming and computationally expensive task. To overcome this issue, embedded systems can be used to speed up the calculation of stereo matching results. the silicon retina delivers asynchronous events if the illumination changes instead of synchronous intensity or color images. It provides sparse input data and therefore the output of the stereo vision algorithm (depth map) is also sparse. the high temporal resolution of such event-driven sensors leads to high data rates. To handle these and the correspondence problem in real time, we implemented our stereo matching algorithm for a field programmable gate array (FPGA). the results show that our matching criterion, based on the time of occurrence of an event, leads to a small average distance error and the parallel hardware architecture and efficient memory utilization results in a frame rate of up to 1140fps.

关键词： Stereo image processing

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Understanding Objects in Detail with Fine-grained Attributes 27

Understanding Objects in Detail with Fine-grained Attributes

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Vedaldi, Andrea Mahendran, Siddharth Tsogkas, Stavros Maji, Subhransu Girshick, Ross Kannala, Juho Rahtu, Esa Kokkinos, Iasonas Blaschko, Matthew B. Weiss, David Taskar, Ben Simonyan, Karen Saphra, Naomi Mohamed, Sammy Univ Oxford Oxford OX1 2JD England Johns Hopkins Univ Baltimore MD 21218 USA Ecole Cent Paris INRIA Saclay Paris France Toyota Res Inst Chicago Chicago IL USA Univ Calif Berkeley Berkeley CA 94720 USA Univ Oulu SF-90100 Oulu Finland Google Res Mountain View CA USA Univ Washington Seattle WA 98195 USA SUNY Stony Brook Stony Brook NY USA

ISBN: (纸本)9781479951178

We study the problem of understanding objects in detail, intended as recognizing a wide array of fine-grained object attributes. To this end, we introduce a dataset of 7,413 air-planes annotated in detail with parts and their attributes, leveraging images donated by airplane spotters and crowd-sourcing both the design and collection of the detailed annotations. We provide a number of insights that should help researchers interested in designing fine-grained datasets for other basic level categories. We show that the collected data can be used to study the relation between part detection and attribute prediction by diagnosing the performance of classifiers that pool information from different parts of an object. We note that the prediction of certain attributes can benefit substantially from accurate part detection. We also show that, differently from previous results in object detection, employing a large number of part templates can improve detection accuracy at the expenses of detection speed. We finally propose a coarse-to-fine approach to speed up detection through a hierarchical cascade algorithm.

关键词： attribute coarse to fine crowd sourcing dataset object description object detection object recognition part

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the Sightfield: Visualizing computer vision, and seeing its capacity to "see"

The Sightfield: Visualizing Computer Vision, and seeing its ...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Mann, Steve Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 1A1 Canada

ISBN: (纸本)9781479943098

computer vision is embedded in toilets, urinals, handwash faucets (e.g. Delta Faucet's 128 or 1024 pixel linear arrays), doors, lightswitches, thermostats, and many other objects that "watch" us. Camera-based motion sensing streetlights are installed throughout entire cities, making embedded vision ubiquitous. Technological advancement is leading to increased sensory and computational performance combined with miniaturization that is making vision sensors less visible. In that sense, computer vision is "seeing" better while it is becoming harder for us to see it. I will introduce and describe the concept of a "sight-field", a time-reversed lightfield that can be visualized with time-exposure photography, to make vision (i.e. the capacity to see) visible. In particular, I will describe a special wand that changes color when it is being observed. the wand has an array of light sources that each change color when being watched. the intensity of each light source increases in proportion to the degree to which it is observed. the wand is a surveillometer/sousveillometer array sensing, measuring, and making visible sur/sousveillance. Moving the wand through space, while tracking its exact 3D position in space, makes visible the otherwise invisible "rays of sight" that emenate from cameras. this capacity to sense, measure, and visualize vision, is useful in liability, insurance, safety, and risk assessment, as well as privacy/priveillance assessment, criminology, urban planning, design, and (sur/sous)veillance studies.

关键词： computer vision

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Automatic Target recognition in Infrared Imagery Using Dense HOG Features and Relevance Grouping of Vocabulary

Automatic Target Recognition in Infrared Imagery Using Dense...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Khan, M. N. A. Fan, Guoliang Heisterkamp, Douglas R. Yu, Liangjiang Oklahoma State Univ Dept Comp Sci Stillwater OK 74078 USA Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

ISBN: (纸本)9781479943098

We study automatic target recognition (ATR) in infrared (IR) imagery by applying two recent computer vision techniques, Histogram of Oriented Gradients (HOG) and Bag-of-Words (BoW). We propose the idea of dense HOG features which are extracted from a set of high-overlapped local patches in a small IR chip and we apply a vocabulary tree that is learned from a set of training images to support efficient and scalable BoW-based ATR. We develop a relevance grouping of vocabulary (RGV) technique to improve the ATR performance by additional voting from grouped visual words. Different from traditional word grouping techniques, RGV groups visual words of the same dominant class to enhance the voting confidence in BoW-based classification. the proposed ATR algorithm is evaluated against recent sparse representation-based classification (SRC) approaches that reportedly outperform traditional methods. Experimental results on the COMANCHE IR dataset demonstrate the advantages of the newly proposed algorithm (BoW-RGV) over the recent SRC approaches.

关键词： Automatic target recognition

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Video-based Object recognition using Novel Set-of-Sets Representations

Video-based Object Recognition using Novel Set-of-Sets Repre...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Liu, Yang Jang, Youngkyoon Woo, Woontack Kim, Tae-Kyun Imperial Coll London London England Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781479943098

We address the problem of object recognition in egocentric videos, where a user arbitrarily moves a mobile camera around an unknown object. Using a video that captures variation in an object's appearance owing to camera motion (more viewpoints, scales, clutter and lighting conditions), can accumulate evidence and improve object recognition accuracy. Most previous work has taken a single image as input, or tackled a video simply by a collection i.e. sum of frame-based recognition scores. In this paper, beyond frame-based recognition, we propose two novel set-of-sets representations of a video sequence for object recognition. We combine the techniques of bag of words for a set of data spatially distributed thus heterogeneous, and manifold for a set of data temporally smooth and homogeneous, to construct the two proposed set-of-sets representations. We also propose methods to perform matching for the two representations respectively. the representations and matching techniques are evaluated on our video-based object recognition datasets, which contain 830 videos of ten objects and four environmental variations. the experiments on the challenging new datasets show that our proposed solution significantly outperforms the traditional frame-based methods.

关键词： object recognition set-of-sets representations video analysis

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Active Planning, Sensing and recognition Using a Resource-Constrained Discriminant POMDP

Active Planning, Sensing and Recognition Using a Resource-Co...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wang, Zhaowen Wang, Zhangyang Moll, Mark Huang, Po-Sen Grady, Devin Nasrabadi, Nasser Huang, thomas Kavraki, Lydia Hasegawa-Johnson, Mark Univ Illinois Beckman Inst Urbana IL 61801 USA Rice Univ Dept Comp Sci Houston TX 77005 USA US Army Res Lab Adelphi MD 20783 USA

ISBN: (纸本)9781479943098

In this paper, we address the problem of object class recognition via observations from actively selected views/modalities/features under limited resource budgets. A Partially Observable Markov Decision Process (POMDP) is employed to find optimal sensing and recognition actions with the goal of long-term classification accuracy. Heterogeneous resource constraints - such as motion, number of measurements and bandwidth - are explicitly modeled in the state variable, and a prohibitively high penalty is used to prevent the violation of any resource constraint. To improve recognition performance, we further incorporate discriminative classification models with POMDP, and customize the reward function and observation model correspondingly. the proposed model is validated on several data sets for multi-view, multi-modal vehicle classification and multi-view face recognition, and demonstrates improvement in both recognition and resource management over greedy methods and previous POMDP formulations.

关键词： Face recognition

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End-to-End Instance Segmentation with Recurrent Attention 30

End-to-End Instance Segmentation with Recurrent Attention

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ren, Mengye Zemel, Richard S. Univ Toronto Toronto ON Canada Canadian Inst Adv Res Toronto ON Canada

ISBN: (纸本)9781538604571

While convolutional neural networks have gained impressive success recently in solving structured prediction problems such as semantic segmentation, it remains a challenge to differentiate individual object instances in the scene. Instance segmentation is very important in a variety of applications, such as autonomous driving, image captioning, and visual question answering. Techniques that combine large graphical models with low-level vision have been proposed to address this problem;however, we propose an end-to-end recurrent neural network (RNN) architecture with an attention mechanism to model a human-like counting process, and produce detailed instance segmentations. the network is jointly trained to sequentially produce regions of interest as well as a dominant object segmentation within each region. the proposed model achieves competitive results on the CVPPP [27], KITTI [12], and Cityscapes [8] datasets.

关键词： Image segmentation Indexes Computational modeling Training Convolution computer vision

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CNN Features off-the-shelf: an Astounding Baseline for recognition

CNN Features off-the-shelf: an Astounding Baseline for Recog...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Razavian, Ali Sharif Azizpour, Hossein Sullivan, Josephine Carlsson, Stefan KTH Royal Inst Technol CVAP Stockholm Sweden

ISBN: (纸本)9781479943098

Recent results indicate that the generic descriptors extracted from the convolutional neural networks are very powerful. this paper adds to the mounting evidence that this is indeed the case. We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the OverFeat network which was trained to perform object classification on ILSVRC13. We use features extracted from the OverFeat network as a generic image representation to tackle the diverse range of recognition tasks of object image classification, scene recognition, fine grained recognition, attribute detection and image retrieval applied to a diverse set of datasets. We selected these tasks and datasets as they gradually move further away from the original task and data the OverFeat network was trained to solve. Astonishingly, we report consistent superior results compared to the highly tuned state-of-the-art systems in all the visual classification tasks on various datasets. For instance retrieval it consistently outperforms low memory footprint methods except for sculptures dataset. the results are achieved using a linear SVM classifier (or L2 distance in case of retrieval) applied to a feature representation of size 4096 extracted from a layer in the net. the representations are further modified using simple augmentation techniques e.g. jittering. the results strongly suggest that features obtained from deep learning with convolutional nets should be the primary candidate in most visual recognition tasks.

关键词： Image representation

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