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Action and Interaction recognition in First-person videos

Action and Interaction Recognition in First-person videos

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

作者： Narayan, Sanath Kankanhalli, Mohan S. Ramakrishnan, Kalpathi R. IISc Dept Elect Engg Bangalore Karnataka India NUS Sch Comp Singapore Singapore

ISBN: (纸本)9781479943098

In this work, we evaluate the performance of the popular dense trajectories approach on first-person action recognition datasets. A person moving around with a wearable camera will actively interact with humans and objects and also passively observe others interacting. Hence, in order to represent real-world scenarios, the dataset must contain actions from first-person perspective as well as thirdperson perspective. For this purpose, we introduce a new dataset which contains actions from both the perspectives captured using a head-mounted camera. We employ a motion pyramidal structure for grouping the dense trajectory features. the relative strengths of motion along the trajectories are used to compute different bag-of-words descriptors and concatenated to form a single descriptor for the action. the motion pyramidal approach performs better than the baseline improved trajectory descriptors. the method achieves 96.7% on the JPL interaction dataset and 61.8% on our NUS interaction dataset. the same is used to detect actions in long video sequences and achieves average precision of 0.79 on JPL interaction dataset.

关键词： First-person video Interaction recognition

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Towards autonomous navigation of miniature UAV

Towards autonomous navigation of miniature UAV

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

作者： Brockers, Roland Humenberger, Martin Weiss, Stephan Matthies, Larry Jet Prop Lab Pasadena CA 91109 USA Austrian Inst Technol Vienna Austria

ISBN: (纸本)9781479943098

Micro air vehicles such as miniature rotorcrafts require high-precision and fast localization updates for their control, but cannot carry large payloads. therefore, only small and light-weight sensors and processing units can be deployed on such platforms, favoring vision-based solutions that use light weight cameras and run on small embedded computing platforms. In this paper, we propose a navigation framework to provide a small quadrotor UAV with accurate state estimation for high speed control including 6DoF pose and sensor self-calibration. Our method allows very fast deployment without prior calibration procedures literally rendering the vehicle a throw-and-go platform. Additionally, we demonstrate hazard-avoiding autonomous landing to showcase a high-level navigation capability that relies on the low-level pose estimation results and is executed on the same embedded platform. We explain our hardware-specific implementation on a 12g processing unit and show real-world end-to-end results.

关键词： Micro Air Vehicle autonomous landing autonomous navigation embedded computing vision based state estimation

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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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Understanding the Nature of First-Person Videos: Characterization and Classification using Low-Level Features

Understanding the Nature of First-Person Videos: Characteriz...

引用

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

作者： Tan, Cheston Goh, Hanlin Chandrasekhar, Vijay Li, Liyuan Lim, Joo-Hwee Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781479943098

First-person view (FPV) video data is set to proliferate rapidly, due to many consumer wearable-camera devices coming onto the market. Research into FPV (or "egocentric") vision is also becoming more common in the computer vision community. However, it is still unclear what the fundamental characteristics of such data are. How is it really different from third-person view (TPV) data? Can all FPV data be treated the same? In this first attempt to approach these questions in a quantitative and empirical manner, we analyzed a meta-collection of 21 FPV and TPV datasets totaling more than 165 hours of video. We performed the first quantitative characterization of FPV videos over multiple datasets, encompassing virtually all available FPV datasets. Validating this characterization, linear classifiers trained on low-level features to perform FPVversus-TPV classification achieved good baseline performance. Accuracy peaked at 81% for 2-minute clips, but 67% accuracy was achieved even with 1-second clips. Our low-level features are fast to compute and do not require annotation. Overall, our work uncovered insights regarding the basic nature and characteristics of FPV data.

关键词： LOW LEVEL Videotapes Pepper golden mosaic virus Dataset images computer vision clipping Clip Camera device

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An Embedded Solution to Visual Mapping for Consumer Drones

An Embedded Solution to Visual Mapping for Consumer Drones

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

作者： Zhou, Guyue Li, Zexiang Liu, Ang Yang, Kang Wang, Tao Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Hong Kong Peoples R China Dafrang Innovat Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781479943098

In this paper, we propose a real-time visual mapping scheme which can be implemented on a low-cost embedded system for consumer-level ratio control (RC) drones. In our work, a 3-dimensional occupancy grid map is obtained based on an estimated trajectory from data fusion of multiple on-board sensors, composed of two downward-facing cameras, two forward-facing cameras, a GPS receiver, a magnetic compass and an inertial measurement unit (IMU) with 3-axis accelerometers and gyroscopes. Taking the advantages of the low-cost FPGA and ARM NEON intrinsics, we run our visual odometry and mapping algorithms at 10Hz on board. Meanwhile, we also present a hierarchical multi-sensor fusion algorithm to provide a robust trajectory for mapping usage. Finally, we verify the feasibility of our approaches and serval potential applications with experimental results in complex indoor/outdoor environments.

关键词： embedded vision sensor fusion visual mapping Sensor fusion MAPPING(MAthEMATICAL) drone honey bees Gyroscopes GPS Receiver Visualization Data fusion Cameras

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Optimization of Iris Codes for Improved recognition

Optimization of Iris Codes for Improved Recognition

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

作者： Mahadeo, Nitin K. Paplinski, Andrew P. Ray, Sid Monash Univ Clayton Sch Informat Technol Clayton Vic 3800 Australia

ISBN: (纸本)9781479943098

the texture of the iris is commonly represented as an iris code in iris recognition systems. While several approaches have been presented for generating iris codes, relatively few comparison techniques have been proposed. In this paper, we take advantage of the availability of several frames from an iris video to create a single optimized iris code. this is achieved by performing both row-wise and column-wise optimization of iris codes. Inconsistent bits are accurately detected and masked in the final iris code. Our experiments demonstrate that by exploiting variations within the comparison scores of different rows and columns of N frames, we are able to derive the number of consistent bits in the final iris code thereby resulting in significant improvement in recognition performance. We compare our algorithm with well-known methods, namely, Fragile bit masking, Signal fusion and, two Score Fusion techniques. Experimental results on a dataset of 986 iris videos show that the proposed method is encouraging and comparable to the best algorithms in the current literature. To our knowledge, this is the first work that makes use of the best rows and columns from different frames in an iris video to improve performance.

关键词： Biometrics Iris Code Iris recognition Matching Performance

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A Train Station Surveillance System: Challenges and Solutions

A Train Station Surveillance System: Challenges and Solution...

引用

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

作者： Ozer, Burak Wolf, Marilyn Perkasie United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9781479943098

In this paper, we describe our multi-year development effort to create a smart-camera surveillance system for use in train stations. We address the problem of activity analysis in very crowded areas and offer solutions to the detection/tracking problems in one of the most crowded environments. the smart camera system is installed in several train stations around Tokyo, Japan and is currently deployed analyzing crowd movement and individual gestures of the passengers in the stations. the paper summarizes the technical problems we faced during our research /development stages as well as during system deployment. the hierarchical smart camera system, which can detect the overall crowd movement in the higher level and individual activities in the lower level, is developed by Verificon Corp., a Princeton University startup company. We developed smart camera algorithms and spent several years commercializing the technology in a joint project with Yokogawa Electric. A railway company was the lead customer on this project. Verificon system passed several rounds of testing, running for several months on live data in several Tokyo train stations and was deployed by the railway company. this project has led to several extensions of the original technology.

关键词： surveillance tracking gesture recognitionin

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

Towards Automated Understanding of Student-Tutor Interaction...

引用

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

Automatic Target Recognition in Infrared Imagery Using Dense...

引用

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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