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ProTractor: A Lightweight Ground Imaging and Analysis System for Early-Season Field Phenotyping

ProTractor: A Lightweight Ground Imaging and Analysis System...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nico Higgs Blanche Leyeza Jordan Ubbens Josh Kocur William van der Kamp Theron Cory Christina Eynck Sally Vail Mark Eramian Ian Stavness Department of Computer Science University of Saskatchewan Saskatoon Canada Saskatoon Research and Development Center Agriculture and Agri-food Canada Saskatoon Canada

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Acquiring high-resolution images in the field for image-based crop phenotyping is typically performed by complicated, custom built "pheno-mobiles." In this paper, we demonstrate that large datasets of crop row images can be easily acquired with consumer cameras attached to a regular tractor. Localization and labeling of individual rows of plants are performed by a computer vision approach, rather than sophisticated real-time geo-location hardware on the tractor. We evaluate our approach for cropping rows of early-season plants from a Brassica carinata field trial where we achieve 100% recall and 99% precision. We also demonstrate a proof-of-concept plant counting method for our ProTractor system using an object detection network that achieves a mean average precision of 0.82 when detecting plants, and an R2 of 0.89 when counting plants. The ProTractor design and software are open source to advance the collection of large outdoor plant phenotyping datasets with inexpensive and easy to use acquisition systems.

关键词： Agricultural machinery Agriculture Cameras computer vision Image segmentation Robot sensing systems

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Motion and Depth Augmented Semantic Segmentation for Autonomous Navigation

Motion and Depth Augmented Semantic Segmentation for Autonom...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hazem Rashed Ahmad El Sallab Senthil Yogamani Mohamed ElHelw Valeo R&D Cairo Egypt Valeo Vision Systems Nile University Cairo Egypt

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Motion and depth provide critical information in autonomous driving and they are commonly used for generic object detection. In this paper, we leverage them for improving semantic segmentation. Depth cues can be useful for detecting road as it lies below the horizon line. There is also a strong structural similarity for different instances of different objects including buildings and trees. Motion cues are useful as the scene is highly dynamic with moving objects including vehicles and pedestrians. This work utilizes geometric information modelled by depth maps and motion cues represented by optical flow vectors to improve the pixel-wise segmentation task. A CNN architecture is proposed and the variations regarding the stage at which color, depth, and motion information are fused, e.g. early-fusion or mid-fusion, are systematically investigated. Additionally, we implement a multimodal fusion algorithm to maximize the benefit from all the information. The proposed algorithms are evaluated on Virtual-KITTI and Cityscapes datasets where results demonstrate enhanced performance with depth and flow augmentation.

关键词： Semantics computer architecture Optical imaging Feature extraction Motion segmentation Optical sensors Estimation

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A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection

A Realistic Dataset and Baseline Temporal Model for Early Dr...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Reza Ghoddoosian Marnim Galib Vassilis Athitsos Vision-Learning-Mining Lab University of Texas at Arlington

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Drowsiness can put lives of many drivers and workers in danger. It is important to design practical and easy-to-deploy real-world systems to detect the onset of drowsiness. In this paper, we address early drowsiness detection, which can provide early alerts and offer subjects ample time to react. We present a large and public real-life dataset of 60 subjects, with video segments labeled as alert, low vigilant, or drowsy. This dataset consists of around 30 hours of video, with contents ranging from subtle signs of drowsiness to more obvious ones. We also benchmark a temporal model for our dataset, which has low computational and storage demands. The core of our proposed method is a Hierarchical Multiscale Long Short-Term Memory (HM-LSTM) network, that is fed by detected blink features in sequence. Our experiments demonstrate the relationship between the sequential blink features and drowsiness. In the experimental results, our baseline method produces higher accuracy than human judgment.

关键词： Cameras Feature extraction Cellular phones Employment Biomedical monitoring Automobiles

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WiFi and vision Multimodal Learning for Accurate and Robust Device-Free Human Activity recognition

WiFi and Vision Multimodal Learning for Accurate and Robust ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Han Zou Jianfei Yang Hari Prasanna Das Huihan Liu Yuxun Zhou Costas J. Spanos Department of Electrical Engineering and Computer Sciences University of California Berkeley USA School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Human activity recognition plays an indispensable role in a myriad of emerging applications in context-aware services. Accurate activity recognition systems usually require the user to carry mobile or wearable devices, which is inconvenient for long term usage. In this paper, we design WiVi, a novel human activity recognition scheme that is able to identify common human activities in an accurate and device-free manner via multimodal machine learning using only commercial WiFi-enabled IoT devices and camera. For sensing using WiFi, a new platform is developed to extract fine-grained WiFi channel information and transform them into WiFi frames. A tailored convolutional neural network model is designed to extract high-level representative features among the WiFi frames in order to provide human activity estimation. We utilized a variant of C3D model for activity sensing using vision. Following this, WiVi performs multimodal fusion at the decision level to combine the strength of WiFi and vision by constructing an ensembled DNN model. Extensive experiments are conducted in an indoor environment, demonstrating that WiVi achieves 97.5% activity recognition accuracy and is robust under unfavorable situations, as each modality provides the complementary sensing when the other faces its limiting conditions.

关键词： Wireless fidelity Activity recognition Cameras Feature extraction Machine learning Privacy

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AnonymousNet: Natural Face De-Identification With Measurable Privacy

AnonymousNet: Natural Face De-Identification With Measurable...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tao Li Lei Lin Department of Computer Science Purdue University Goergen Institute for Data Science University of Rochester

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

With billions of personal images being generated from social media and cameras of all sorts on a daily basis, security and privacy are unprecedentedly challenged. Although extensive attempts have been made, existing face image de-identification techniques are either insufficient in photo-reality or incapable of balancing privacy and usability qualitatively and quantitatively, i.e., they fail to answer counterfactual questions such as "is it private now?", "how private is it?", and "can it be more private?" In this paper, we propose a novel framework called AnonymousNet, with an effort to address these issues systematically, balance usability, and enhance privacy in a natural and measurable manner. The framework encompasses four stages: facial attribute estimation, privacy-metric-oriented face obfuscation, directed natural image synthesis, and adversarial perturbation. Not only do we achieve the state-of-the-arts in terms of image quality and attribute prediction accuracy, we are also the first to show that facial privacy is measurable, can be factorized, and accordingly be manipulated in a photo-realistic fashion to fulfill different requirements and application scenarios. Experiments further demonstrate the effectiveness of the proposed framework.

关键词： Face Privacy Generative adversarial networks Measurement Gallium nitride Image quality

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Analysis of Deep Fusion Strategies for Multi-Modal Gesture recognition

Analysis of Deep Fusion Strategies for Multi-Modal Gesture R...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Alina Roitberg Tim Pollert Monica Haurilet Manuel Martin Rainer Stiefelhagen CV:HCI Lab Karlsruhe Institute of Technology Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Video-based gesture recognition has a wide spectrum of applications, ranging from sign language understanding to driver monitoring in autonomous cars. As different sensors suffer from their individual limitations, combining multiple sources has strong potential to improve the results. A number of deep architectures have been proposed to recognize gestures from e.g. both color and depth data. However, these models conventionally comprise separate networks for each modality, which are then combined in the final layer (e.g. via simple score averaging). In this work, we take a closer look at different fusion strategies for gesture recognition especially focusing on the information exchange in the intermediate layers. We compare three fusion strategies on the widely used C3D architecture: 1) late fusion, combining the streams in the final layer; 2) information exchange in an intermediate layer using an additional convolution layer; and 3) linking information at multiple layers simultaneously using the cross-stitch units, originally designed for multi-task learning. Our proposed C3D-Stitch model achieves the best recognition rate, demonstrating the effectiveness of sharing information at earlier stages.

关键词： Gesture recognition Convolution computer architecture Image color analysis Data models Videos Correlation

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Sports Camera Calibration via Synthetic Data

Sports Camera Calibration via Synthetic Data

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jianhui Chen James J. Little University of British Columbia

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Calibrating sports cameras is important for autonomous broadcasting and sports analysis. Here we propose a highly automatic method for calibrating sports cameras from a single image using synthetic data. First, we develop a novel camera pose engine that generates camera poses by randomly sampling camera parameters. The camera pose engine has only three significant free parameters so that it can effectively generate diverse camera poses and corresponding edge (i.e. field marking) images. Then, we learn compact feature descriptors via a siamese network from paired edge images and build a feature-pose database. After that, we use a novel GAN (generative adversarial network) model to detect field markings in real images. Finally, we query an initial camera pose from the feature-pose database and refine camera poses using truncated distance images. We evaluate our method on both synthetic and real data. Our method not only demonstrates the robustness on the synthetic data but also achieves state-of-the-art accuracy on a standard soccer dataset and very high performance on a volleyball dataset.

关键词： Cameras Image edge detection Gallium nitride Calibration Robot vision systems Feature extraction

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FaceGenderID: Exploiting Gender Information in DCNNs Face recognition Systems

FaceGenderID: Exploiting Gender Information in DCNNs Face Re...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ruben Vera-Rodriguez Marta Blazquez Aythami Morales Ester Gonzalez-Sosa João C. Neves Hugo Proença Biometrics and Data Pattern Analytics - BiDA Lab Universidad Autonoma de Madrid Madrid Spain Nokia Bell Labs Madrid Spain University of Beira Interior Covilha Portugal

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

This paper addresses the effect of gender as a covariate in face verification systems. Even though pre-trained models based on Deep Convolutional Neural Networks (DCNNs), such as VGG-Face or ResNet-50, achieve very high performance, they are trained on very large datasets comprising millions of images, which have biases regarding demographic aspects like the gender and the ethnicity among others. In this work, we first analyse the separate performance of these state-of-the-art models for males and females. We observe a gap between face verification performances obtained by both gender classes. These results suggest that features obtained by biased models are affected by the gender covariate. We propose a gender-dependent training approach to improve the feature representation for both genders, and develop both: i) gender specific DCNNs models, and ii) a gender balanced DCNNs model. Our results show significant and consistent improvements in face verification performance for both genders, individually and in general with our proposed approach. Finally, we announce the availability (at GitHub) of the FaceGenderID DCNNs models proposed in this work, which can support further experiments on this topic.

关键词： Face Face recognition Training Databases Estimation Data models

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A Guided Multi-Scale Categorization of Plant Species in Natural Images

A Guided Multi-Scale Categorization of Plant Species in Natu...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jonas Krause Kyungim Baek Lipyeow Lim Dept. of Information and Computer Sciences University of Hawai'i at Manoa Honolulu HI

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Automatic categorization of plant species in natural images is an important computer vision problem with numerous applications in agriculture and botany. The problem is particularly challenging due to the large number of plant species, the inter-species similarity, the large scale variations in natural images, and the lack of annotated data. In this paper, we present a guided multi-scale approach that segments the regions of interest (containing a plant) from a complex background of the natural image and systematically extracts scale-representative patches based on those regions. These multi-scale patches are used to train state-of-the-art Convolutional Neural Network (CNN) models that analyze a given plant image and determine its species. Focusing specifically on the identification of plant species in natural images, we show that the proposed approach is a very effective way of making deep learning models more robust to scale variations. We perform a comprehensive experimental evaluation of our proposed method over several CNN models. Our best result on the Inception-ResNet-v2 model achieves a top-1 classification accuracy of 89.21% for 100 plant species which represents a 5.4% increase over using random cropping to generate training data.

关键词： Image segmentation Birds Feature extraction Biological system modeling Streaming media computer vision Head

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ieee computer Society conference on computer vision and pattern recognition workshops

IEEE Computer Society Conference on Computer Vision and Patt...

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31st Meeting of the ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2018

ISBN: (纸本)9781538661000

The proceedings contain 307 papers. The topics discussed include: deep features for recognizing disguised faces in the wild;unconstrained fingerphoto database;hybrid user-independent and user-dependent offline signature verification with a two-channel CNN;it takes two to tango: cascading off-the-shelf face detectors;time analysis of pulse-based face anti-spoofing in visible and NIR;a deep face identification network enhanced by facial attributes prediction;gait recognition by deformable registration;and fusion of handcrafted and deep learning features for large-scale multiple iris presentation attack detection.

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