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Traffic analysis with low frame rate camera networks

Traffic analysis with low frame rate camera networks

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tae Eun Choe Mun Wai Lee Niels Haering Object Video Inc. Reston VA USA

We propose a new traffic analysis framework using existing traffic camera networks. The framework integrates vehicle detection and image-based matching methods with geographic context to match vehicles across different views and analyze traffic. This is a challenging problem due to the low frame-rate of traffic-cams and the large distance between views. A vehicle may not always appear in a camera due to large inter-frame interval or inter-occlusion. We applied the proposed method to a traffic camera network to detect and track key vehicles to analyze traffic condition. Vehicles are detected using a multi-view approach. By integrating camera calibration information and GIS data, we extract traffic lane information and prior knowledge of expected vehicle orientation and image size at each image location. This improves detection speed and reduces false alarms by discarding unlikely scale and orientation. Subsequently, detected vehicles are matched across cameras using a view-invariant appearance model. For more accurate vehicle matching, traffic patterns observed at two sets of cameras are temporally aligned. Finally, key vehicles are globally tracked across cameras using the max-flow/min-cut network tracking algorithm. Traffic conditions at each camera location are presented on a map.

关键词： Telecommunication traffic Cameras Vehicle detection Vehicles Traffic control Image analysis Calibration Geographic Information Systems Data mining pattern matching

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Mushroom Segmentation and 3D Pose Estimation from Point Clouds using Fully Convolutional Geometric Features and Implicit Pose Encoding

Mushroom Segmentation and 3D Pose Estimation from Point Clou...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： George Retsinas Niki Efthymiou Petros Maragos School of Electrical and Computer Engineering National Technical University of Athens Greece

Modern agricultural applications rely more and more on deep learning solutions. However, training well-performing deep networks requires a large amount of annotated data that may not be available and in the case of 3D annotation may not even be feasible for human annotators. In this work, we develop a deep learning approach to segment mushrooms and estimate their pose on 3D data, in the form of point clouds acquired by depth sensors. To circumvent the annotation problem, we create a synthetic dataset of mushroom scenes, where we are fully aware of 3D information, such as the pose of each mushroom. The proposed network has a fully convolutional backbone, that parses sparse 3D data, and predicts pose information that implicitly defines both instance segmentation and pose estimation task. We have validated the effectiveness of the proposed implicit-based approach for a synthetic test set, as well as provided qualitative results for a small set of real acquired point clouds with depth sensors.

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Exploiting local and global scene constraints in modeling large-scale dynamic 3D scenes from aerial video

Exploiting local and global scene constraints in modeling la...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hao Tang Zhigang Zhu Department of Computer Science CUNY Graduate Center New York NY USA Department of Computer Science City College of New York New York NY USA

This paper presents a framework to analyze a large amount of video data and extract high-level structural information - planar structures and motion information - in typical urban scenes, which may be used in video coding or object recognition. The method consists of two phases. In the first phase, multiple parallel-perspective (pushbroom) mosaics are generated from the video data. In the second phase, the planar structures and the moving objects are extracted from the mosaics by a segmentation-based stereo match method. The focus of this paper is the use of local and global scene constraints to improve the accuracy of high-level structural information extraction. The 3D planar patches obtained from the first step of 3D reconstruction are automatically clustered into one or more dominant planes, which are typical in indoor or outdoor city scene and are used to improve the 3D model. Then a local scene constraint is used to further refine the structure of a patch from the structures of its neighboring patches that have better structure estimations. Further, the dominant planes also provide information of road network directions, which greatly facilitates the search of moving objects on the roads. We demonstrate the effectiveness of our approach by experiments on a real video data set of a New York City scene.

关键词： Layout Large-scale systems Cities and towns Cameras Video sequences Data mining computer science Image reconstruction Buildings Roads

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Non-rigid articulated point set registration with Local Structure Preservation

Non-rigid articulated point set registration with Local Stru...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Song Ge Guoliang Fan School of Electrical and Computer Engineering Oklahoma State University Stillwater OK

ISBN: (纸本)9781467367608

We propose a new Gaussian mixture model (GMM)-based probabilistic point set registration method, called Local Structure Preservation (LSP), which is aimed at complex non-rigid and articulated deformations. LSP integrates two complementary shape descriptors to preserve the local structure. The first one is the Local Linear Embedding (LLE)-based topology constraint to retain the local neighborhood relationship, and the other is the Laplacian Coordinate (LC)-based energy to encode the local neighborhood scale. The registration is formulated as a density estimation problem where the LLE and LC terms are embedded in the GMM-based Coherent Point Drift (CPD) framework. A closed form solution is solved by an Expectation Maximization (EM) algorithm where the two local terms are jointly optimized along with the CPD coherence constraint. The experimental results on a challenging 3D human dataset show the accuracy and efficiency of our proposed approach to handle non-rigid highly articulated deformations.

关键词： Yttrium Estimation Shape Laplace equations Joints Coherence Optimization

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Categorization in natural time-varying image sequences

Categorization in natural time-varying image sequences

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Teresa Ko Stefano Soatto Deborah Estrin Vision Laboratory University of California Los Angeles Los Angeles CA USA

Approaches to single image categorization do not easily generalize to natural time-varying image sequences. In natural environments, object categories tend to have few features that help to distinguish between each other and the surrounding environment. To better discriminate between categories and the surrounding environment, we propose a multi-view categorization approach that exploits the statistics of image sequences rather than single images. The approach is unbiased towards redundant views - that is, it does not matter how many times an object appears from the same viewpoint. At the same time, the approach does not penalize for missing views, so that we do not have to capture an object at all viewpoints to successfully categorize the object. We first present a data set for studying natural environment monitoring: an image sequence of birds at a feeder station. After manual localization, a baseline bag of features approach was found to perform significantly worse on the proposed data set compared to the standard Caltech 101 data set. We find that our approach increases the categorization accuracy from 48% to 58% on average when compared to an equivalent single view categorization method. Finally, we show how the same metric proposed for the supervised categorization can be used to transform, in an unsupervised manner, an image sequence into a manageable set of categories.

关键词： Image sequences Monitoring Birds Surveillance Histograms Face detection Spatial resolution Testing Statistics Costs

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Hardware implementation of a binary neural image processor

Hardware implementation of a binary neural image processor

引用

Proceedings of the 5th International conference on Image Processing and its Applications

作者： Kennedy, J.V. Austin, J. Cass, B. Univ of York United Kingdom

This paper presents the work that has resulted in the SAT processor;a dedicated hardware implementation of a binary neural image processor. The SAT processor is aimed specifically at supporting the ADAM algorithm and is currently being integrated into a new version of the C-NNAP parallel image processor. The SAT processor performs binary matrix multiplications, a task that is computationally complex for a CPU with a standard instruction set. It can perform the matrix multiplication and thresholding between 100 and 200 times faster than the DSP32C that uses an in-house produced dedicated coprocessor. This speed-up will allow the SAT to process images of up to 220 × 220 pixels at 25-Hz frame rates.

关键词： computer hardware

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3D Face recognition Using Two Views Face Modeling and Labeling

3D Face Recognition Using Two Views Face Modeling and Labeli...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yi Sun Lijun Yin Computer Science Department State University of New York Binghamton Binghamton NY USA

The ability to distinguish different people by using 3D facial information is an active research problem being undertaken by the face recognition community. In this paper, we propose to use a generic model to label the 3D facial features. This approach relies on our realistic face modeling technique, by which the individual face model is created using a generic model and two views of a face. In the individualized model, we label the face features by their maximum and minimum curvatures. Among the labeled features, the "good features" are selected by using a Genetic Algorithm based approach. The feature space is then formed by using these new 3D shape descriptors, and each individual face is classified according to its feature space correlation. We applied 72 individual models for the test. The experimental results show that the shape information obtained from the 3D individualized model can be used to classify and identify individual facial surfaces. The rank-four correct recognition rate is about 92%. The 3D individualized model provides consistent and sufficient details to represent individual faces while using a much more simpli?ed representation than the range data models. This work provides the possibility to reduce the complexity of 3D data processing, and is feasible for real applications such as in a non-cooperative imaging circumstance or in the situation when range data using 3D scanners are not possible to acquire.

关键词： Face recognition Labeling Shape Facial animation Data processing Sun computer science Facial features Genetic algorithms Testing

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Multispectral Contrastive Learning with Viewmaker Networks

Multispectral Contrastive Learning with Viewmaker Networks

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jasmine Bayrooti Noah Goodman Alex Tamkin Department of Computer Science Stanford University Stanford CA USA

Contrastive learning methods have been applied to a range of domains and modalities by training models to identify similar "views" of data points. However, specialized scientific modalities pose a challenge for this paradigm, as identifying good views for each scientific instrument is complex and time-intensive. In this paper, we focus on applying contrastive learning approaches to a variety of remote sensing datasets. We show that Viewmaker networks, a recently proposed method for generating views without extensive domain knowledge, can produce useful views in this setting. We also present a Viewmaker variant called Divmaker, which achieves similar performance and does not require adversarial optimization. Applying both methods to four multispectral imaging problems, each with a different format, we find that Viewmaker and Divmaker can outperform cropping- and reflection-based methods for contrastive learning in every case when evaluated on downstream classification tasks. This provides additional evidence that domain-agnostic methods can empower contrastive learning to scale to real-world scientific domains. Open source code can be found at https://***/jbayrooti/divmaker.

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引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ravikiran Krishnan Sudeep Sarkar Department of Computer Science and Engineering University of South Florida Tampa FL USA

One of the dominant approaches to gesture recognition, especially when we have one or few samples per class, is to compute the time-warped distance between the two sequences and perform nearest-neighbor classification. In this work, we show that we get much better results if instead we consider the similarity of the pattern of frame-wise distances of these two sequences with a third (anchor) sequence from the modelbase. We refer to these distance pattern vectors as the warp vectors. If these warp vectors are similar, then so are the sequences, if not, they are dissimilar. At the algorithmic core we have two dynamic time warping processes, one to compute the warp vectors with the anchor sequences and the other to compare these warp vectors. We select the anchor sequence to be the one that minimizes the overall distance, i.e. the sequence with respect to which these two sequences are the most similar. We present results on a large dataset of 1500 RGBD sequences spanning 150 gesture classes, such as traffic signals, sign language, and every day actions, extracted from the ChaLearn Gesture Challenge dataset. We experimented with three different feature types: difference of frames, HOG and relational distributions. We found that there were improvements of 5%, 15%, and 7%, respectively, at 20% false alarm rate, over traditional two-sequence based timewarped distance.

关键词： Vectors Computational modeling Hidden Markov models Gesture recognition Mathematical model Assistive technology Equations

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A kidney segmentation approach from DCE-MRI using level sets

A kidney segmentation approach from DCE-MRI using level sets

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： H. Abdelmunim Aly A. Farag W. Miller Mohamed AboelGhar Department of Computer Science Computational Biomedicine Laboratory University of Houston Houston TX USA Computer Vision and Image Processing LabDepartment of Electrical and Computer Engineering University of Louisville Louisville KY USA Medical School Mansoura University Mansoura Egypt

Acute rejection is the most common reason of graft failure after kidney transplantation, and early detection is crucial to survive the transplanted kidney function. Automatic classification of normal and acute rejection transplants from dynamic contrast enhanced magnetic resonance imaging (DCEMRI), is of great importance. Kidney segmentation is the first step for such classification. The image intensity inside the kidney is used as an indication of failure/success. Differentiating between different cases cases is implemented by comparing subsequential kidney scans signals. So, this process is mainly dependent on segmentation. This paper introduces a new shape-based segmentation approach based on level sets. Training shapes are collected from different real data sets to represent the shape variations. Signed distance functions are used to represent these shapes. The methodology incorporates image and shape prior information in a variational framework. The shape registration is considered the backbone of the approach where more general transformations can be used to handle the process. We introduce a novel shape dissimilarity measure that enables the use of different (inhomogeneous) scales. The approach gives successful results compared with other techniques restricted to transformations with homogeneous scales. Results for segmenting kidney images will be illustrated and compared with other approaches to show the efficiency of the proposed technique.

关键词： Level set Image segmentation computer vision Magnetic resonance imaging Shape measurement Biopsy Biomedical computing computer science Image processing Biomedical imaging

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