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

作者： Conti, Francesco Pullini, Antonio Benini, Luca Univ Bologna Dept Elect Elect & Informat Engn I-40126 Bologna Italy Swiss Fed Inst Technol Integrated Syst Lab Zurich Switzerland

ISBN: (纸本)9781479943098

Brain-inspired computer vision (BICV) has evolved rapidly in recent years and it is now competitive with traditional CV approaches. However, most of BICV algorithms have been developed on high power-and-performance platforms (e.g. workstations) or special purpose hardware. We propose two different algorithms for counting people in a classroom, both based on Convolutional Neural Networks (CNNs), a state-of-art deep learning model that is inspired on the structure of the human visual cortex. Furthermore, we provide a standalone parallel C library that implements CNNs and use it to deploy our algorithms on the embedded mobile ARM big. LITTLE-based Odroid-XU platform. Our performance and power measurements show that neuromorphic vision is feasible on off-the-shelf embedded mobile platforms, and we show that it can reach very good energy efficiency for non-time-critical tasks such as people counting.

关键词： brain-inspired computer vision classroom occupancy convolutional neural networks odroid people counting

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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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An Introduction to the 3rd Workshop on Egocentric (First-person) vision

An Introduction to the 3rd Workshop on Egocentric (First-per...

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

作者： Mann, Steve Kitani, Kris M. Lee, Yong Jae Ryoo, M. S. Fathi, Alireza

ISBN: (纸本)9781479943098

Egocentric vision provides a unique perspective of the visual world that is inherently human-centric. Since egocentric cameras are mounted on the user (typically on the user's head), they are naturally primed to gather visual information from our everyday interactions, and can even act on that information in real-time (e. g. for a vision aid). We believe that this human-centric characteristic of egocentric vision can have a large impact on the way we approach central computer vision tasks such as visual detection, recognition, prediction, and socio-behavioral analysis. By taking advantage of the first-person point-of-view paradigm, there have been recent advances in areas such as personalized video summarization, understanding concepts of social saliency, activity analysis with inside-out cameras (a camera to capture eye gaze and an outward-looking camera), recognizing human interactions and modeling focus of attention. However, in many ways people are only beginning to understand the full potential (and limitations) of the first-person paradigm. In the 3rd workshop on Egocentric (First-Person) vision, we bring together researchers to discuss emerging topics such as: Personalization of visual analysis;Socio-behavioral modeling;Understanding group dynamics and interactions;Egocentric video as big data;First-person vision for robotics;and Egographical User Interfaces (EUIs).

关键词： Sight Cameras group dynamics Workshops workshops full potential robotics

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DL-SFA: Deeply-Learned Slow Feature Analysis for Action recognition 27

DL-SFA: Deeply-Learned Slow Feature Analysis for Action Reco...

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

作者： Sun, Lin Jia, Kui Chan, Tsung-Han Fang, Yuqiang Wang, Gang Yan, Shuicheng Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore Adv Digital Sci Ctr Singapore Singapore Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Hong Kong Peoples R China Nanyang Technol Univ Singapore 639798 Singapore Lenovo Corp Res Hong Kong Branch Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

Most of the previous work on video action recognition use complex hand-designed local features, such as SIFT, HOG and SURF, but these approaches are implemented sophisticatedly and difficult to be extended to other sensor modalities. Recent studies discover that there are no universally best hand-engineered features for all datasets, and learning features directly from the data may be more advantageous. One such endeavor is Slow Feature Analysis (SFA) proposed by Wiskott and Sejnowski [33]. SFA can learn the invariant and slowly varying features from input signals and has been proved to be valuable in human action recognition [34]. It is also observed that the multi-layer feature representation has succeeded remarkably in widespread machine learning applications. In this paper, we propose to combine SFA with deep learning techniques to learn hierarchical representations from the video data itself. Specifically, we use a two-layered SFA learning structure with 3D convolution and max pooling operations to scale up the method to large inputs and capture abstract and structural features from the video. thus, the proposed method is suitable for action recognition. At the same time, sharing the same merits of deep learning, the proposed method is generic and fully automated. Our classification results on Hollywood2, Kth and UCF Sports are competitive with previously published results. To highlight some, on the Kth dataset, our recognition rate shows approximately 1% improvement in comparison to state-of-the-art methods even without supervision or dense sampling.

关键词： action recognition deep learning slow feature analysis Machine Learning recognition (Psychology) video data Dataset emotional feature analysis Regulated industries Action time sharing

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Streetscore - Predicting the Perceived Safety of One Million Streetscapes

Streetscore - Predicting the Perceived Safety of One Million...

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

作者： Naik, Nikhil Philipoom, Jade Raskar, Ramesh Hidalgo, Cesar MIT Media Lab Cambridge MA 02139 USA

ISBN: (纸本)9781479943098

Social science literature has shown a strong connection between the visual appearance of a city's neighborhoods and the behavior and health of its citizens. Yet, this research is limited by the lack of methods that can be used to quantify the appearance of streetscapes across cities or at high enough spatial resolutions. In this paper, we describe 'Streetscore', a scene understanding algorithm that predicts the perceived safety of a streetscape, using training data from an online survey with contributions from more than 7000 participants. We first study the predictive power of commonly used image features using support vector regression, finding that Geometric Texton and Color Histograms along with GIST are the best performers when it comes to predict the perceived safety of a streetscape. Using Streetscore, we create high resolution maps of perceived safety for 21 cities in the Northeast and Midwest of the United States at a resolution of 200 images/square mile, scoring similar to 1 million images from Google Streetview. these datasets should be useful for urban planners, economists and social scientists looking to explain the social and economic consequences of urban perception.

关键词： internet vision trueskill urban computing urban planning visual analysis beyond semantics

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

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

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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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A Train Station Surveillance System: Challenges and Solutions

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

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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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Distributed Multi-Target Tracking and Data Association in vision Networks

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ieee TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2016年第7期38卷 1397-1397页

作者： Kamal, Ahmed T. Bappy, Jawadul H. Farrell, Jay A. Roy-Chowdhury, Amit K. Univ Calif Riverside Riverside CA 92521 USA

Distributed algorithms have recently gained immense popularity. With regards to computer vision applications, distributed multi-target tracking in a camera network is a fundamental problem. the goal is for all cameras to have accurate state estimates for all targets. Distributed estimation algorithms work by exchanging information between sensors that are communication neighbors. vision-based distributed multi-target state estimation has at least two characteristics that distinguishes it from other applications. First, cameras are directional sensors and often neighboring sensors may not be sensing the same targets, i.e., they are naive with respect to that target. Second, in the presence of clutter and multiple targets, each camera must solve a data association problem. this paper presents an information-weighted, consensus-based, distributed multi-target tracking algorithm referred to as the Multi-target Information Consensus (MTIC) algorithm that is designed to address both the naivety and the data association problems. It converges to the centralized minimum mean square error estimate. the proposed MTIC algorithm and its extensions to non-linear camera models, termed as the Extended MTIC (EMTIC), are robust to false measurements and limited resources like power, bandwidth and the realtime operational requirements. Simulation and experimental analysis are provided to support the theoretical results.

关键词： Consensus distributed tracking data association camera networks

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A Sequential Classifier for Hand Detection in the Framework of Egocentric vision

A Sequential Classifier for Hand Detection in the Framework ...

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

作者： Betancourt, Alejandro Lopez, Miriam M. Regazzoni, Carlo S. Rauterberg, Matthias Univ Genoa Dept Naval Elect Elect & Telecommun Engn I-16126 Genoa Italy Eindhoven Univ Technol Dept Ind Design Designed Intelligence Grp Eindhoven Netherlands

ISBN: (纸本)9781479943098

Hand detection is one of the most explored areas in Egocentric vision Video Analysis for wearable devices. Current methods are focused on pixel-by-pixel hand segmentation, with the implicit assumption of hand presence in almost all activities. However, this assumption is false in many applications for wearable cameras. Ignoring this fact could affect the whole performance of the device since hand measurements are usually the starting point for higher level inference, or could lead to inefficient use of computational resources and battery power. In this paper we propose a two-level sequential classifier, in which the first level, a hand-detector, deals with the possible presence of hands from a global perspective, and the second level, a hand-segmentator, delineates the hand regions at pixel level in the cases indicated by the first block. the performance of the sequential classifier is stated in probabilistic notation as a combination of both, classifiers allowing to test new hand-detectors independently of the type of segmentation and the dataset used in the training stage. Experimental results show a considerable improvement in the detection of true negatives, without compromising the performance of the true positives.

关键词： Hand Detection First Person vision Egocentric vision Video Processing

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