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Implementation of an Anomalous Human Activity recognition System

SN computer Science 2020年第3期1卷 1-10页

作者： Shreyas, D.G. Raksha, S. Prasad, B.G. B. M. S. College of Engineering Bengaluru India

This paper brings to light one of the most prominent applications of human activity recognition which is the anomaly detection. Providing security to an individual is a major concern of any society today due to the constantly increasing actions causing threats, starting from deliberate violence to an injury caused by an accident. Mere installation of a traditional closed-circuit television is not enough as it requires a human being to constantly remain alert and monitor the cameras, which is quite inefficient. This calls for the need to develop an automated security system that identifies abnormal activities in real time and brings immediate help to the victims. But it is time-consuming to recognize activities from lengthy surveillance videos and hence a new implementation idea which adaptively compresses the videos before passing it through the activity recognition system has been proposed. Adaptive video compression is a unique compression technology that compresses only the least significant parts of the video, retaining the objects of interest. By combining adaptive video compression and the convolutional 3D network with contextual multiple scales based on the temporal features (Xu et al. in Learning deep representations of appearance and motion for anomalous event detection. In: computer vision and pattern recognition (cvpr), 2015), the paper aims to provide an accurate anomalous human activity recognition system that works in real time. The method is implemented on the UCF101 crime dataset with around 13 different anomalies (Learning Spatiotemporal Features with 3D Convolutional Networks. In: International conference on computer vision (ICCV), ieee, 2015). It resulted in improved recognition performance compared to state-of-the-art techniques. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Adaptive video compression Anomaly detection Human activity recognition vision-based human activity recognition

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Proceedings of the ieee computer society conference on computer vision and pattern recognition

Proceedings of the IEEE Computer Society Conference on Compu...

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30th ieee conference on computer vision and pattern recognition, cvpr 2017

The proceedings contain 781 papers. The topics discussed include: exclusivity-consistency regularized multi-view subspace clustering;borrowing treasures from the wealthy: deep transfer learning through selective joint fine-tuning;the more you know: using knowledge graphs for image classification;dynamic edge-conditioned filters in convolutional neural networks on graphs;convolutional neural network architecture for geometric matching;deep affordance-grounded sensorimotor object recognition;on compressing deep models by low rank and sparse decomposition;unsupervised pixel-level domain adaptation with generative adversarial networks;photo-realistic single image super-resolution using a generative adversarial network;a practical method for fully automatic intrinsic camera calibration using directionally encoded light;elastic shape-from-template with spatially sparse deforming forces;and distinguishing the indistinguishable: exploring structural ambiguities via geodesic context.

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WiCV 2019: The Sixth Women In computer vision Workshop 32

WiCV 2019: The Sixth Women In Computer Vision Workshop

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Amerini, Irene Balashova, Elena Ebrahimi, Sayna Leonard, Kathryn Nagrani, Arsha Salvador, Amaia Univ Florence Florence Italy Princeton Univ Princeton NJ 08544 USA Univ Calif Berkeley Berkeley CA USA Occident Coll Los Angeles CA USA Univ Oxford Oxford England Univ Politen Catalunya Barcelona Spain

ISBN: (纸本)9781728125060

In this paper we present the Women in computer vision Workshop - WiCV 2019, organized in conjunction with cvpr 2019. This event is meant for increasing the visibility and inclusion of women researchers in computer vision field. computer vision and machine learning have made incredible progress over the past years, but the number of female researchers is still low both in the academia and in the industry. WiCV is organized especially for this reason: to raise visibility of female researchers, to increase collaborations between them, and to provide mentorship to female junior researchers in the field. In this paper, we present a report of trends over the past years, along with a summary of statistics regarding presenters, attendees, and sponsorship for the current workshop.

关键词： computer vision

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Isospectralization, or how to hear shape, style, and correspondence 32

Isospectralization, or how to hear shape, style, and corresp...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cosmo, Luca Panine, Mikhail Rampini, Arianna Ovsjanikov, Maks Bronstein, Michael M. Rodola, Emanuele Univ Venice Venice Italy Ecole Polytech Palaiseau France Sapienza Univ Rome Rome Italy USI Imperial Coll London London England

ISBN: (纸本)9781728132938

The question whether one can recover the shape of a geometric object from its Laplacian spectrum ('hear the shape of the drum') is a classical problem in spectral geometry with a broad range of implications and applications. While theoretically the answer to this question is negative (there exist examples of iso-spectral but non-isometric manifolds), little is known about the practical possibility of using the spectrum for shape reconstruction and optimization. In this paper we introduce a numerical procedure called isospectralization, consisting of deforming one shape to make its Laplacian spectrum match that of another We implement the isospectralization procedure using modern differentiable programming techniques and exemplify its applications in some of the classical and notoriously hard problems in geometry processing, computer vision, and graphics such as shape reconstruction,pose and style transfer and dense deformable correspondence.

关键词： Grouping and Shape Physics-based vision and Shape-from-X Segmentation vision + Graphics

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Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising 32

Non-local Meets Global: An Integrated Paradigm for Hyperspec...

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

作者： He, Wei Yao, Quanming Li, Chao Yokoya, Naoto Zhao, Qibin RIKEN AIP Tokyo Japan HKUST Hong Kong Peoples R China

ISBN: (纸本)9781728132938

Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) denoising. Unfortunately, while their denoising performance benefits little from more spectral bands, the running time of these methods significantly increases. In this paper, we claim that the HSI lies in a global spectral low-rank subspace, and the spectral subspaces of each full band patch groups should lie in this global low-rank subspace. This motivates us to propose a unified spatial-spectral paradigm for HSI denoising. As the new model is hard to optimize, An efficient algorithm motivated by alternating minimization is developed. This is done by first learning a low-dimensional orthogonal basis and the related reduced image from the noisy HSI. Then, the non-local low-rank denoising and iterative regularization are developed to refine the reduced image and orthogonal basis, respectively. Finally, the experiments on synthetic and both real datasets demonstrate the superiority against the stateof-the-art HSI denoising methods.

关键词： Low-level vision vision + Graphics

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Recognizing Multi-Modal Face Spoofing with Face recognition Networks 32

Recognizing Multi-Modal Face Spoofing with Face Recognition ...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Parkin, Aleksandr Grinchuk, Oleg VisionLabs Amsterdam Netherlands

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

Detecting spoofing attacks plays a vital role for deploying automatic face recognition for biometric authentication in applications such as access control, face payment, device unlock, etc. In this paper we propose a new anti-spoofing network architecture that takes advantage of multi-modal image data and aggregates intra-channel features at multiple network layers. We also transfer strong facial features learned for face recognition and show their benefits for detecting spoofing attacks. Finally, to increase the generalization ability of our method to unseen attacks, we use an ensemble of models trained separately for distinct types of spoofing attacks. The proposed method achieves state-of-the-art result on the largest multi-modal anti-spoofing dataset CASIA-SURF [26].

关键词： Face Face recognition Task analysis Training Cameras Training data Neural networks

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U-Net based convolutional neural network for skeleton extraction 32

U-Net based convolutional neural network for skeleton extrac...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Panichev, Oleg Voloshyna, Alona Ciklum Amosova Str 12 Kiev Ukraine

ISBN: (纸本)9781728125060

Skeletonization is a process aimed to extract a line-like object shape representation, skeleton, which is of great interest for optical character recognition, shape-based object matching, recognition, biomedical image analysis, etc.. Existing methods for skeleton extraction are typically based on topological, morphological or distance transform and are known to be sensitive to the noise on the boundary and require post-processing procedure for redundant branches pruning. In this work, we introduce U-net based approach for direct skeleton extraction of the object within Pixel SkelNetOn - cvpr 2019 challenge, inspired by CNNs success in skeleton extraction from real images task. The main idea of our approach is to consistently edit a skeleton mask by feature propagation through different scale layers. It opposes final skeleton generation from different scale object shape representations as occurs in approaches with deep supervision for skeleton extraction from the real image. Our U-net based model showed 0.75 F1-score on the validation set and the ensemble of eight identical models, trained on different data subsets, got 0.7846 F1-score on the test data.

关键词： Optical character recognition

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FaceBagNet: Bag-of-local-features Model for Multi-modal Face Anti-spoofing 32

FaceBagNet: Bag-of-local-features Model for Multi-modal Face...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Shen, Tao Huang, Yuyu Tong, Zhijun ReadSense Shanghai Peoples R China

ISBN: (纸本)9781728125060

Face anti-spoofing detection is a crucial procedure in biometric face recognition systems. State-of-the-art approaches, based on Convolutional Neural Networks (CNNs), present good results in this field. However, previous works focus on one single modal data with limited number of subjects. The recently published CASIA-SURF dataset is the largest dataset that consists of 1000 subjects and 21000 video clips with 3 modalities (RGB, Depth and IR). In this paper, we propose a multi-stream CNN architecture called FaceBagNet to make full use of this data. The input of FaceBagNet is patch-level images which contributes to extract spoof-specific discriminative information. In addition, in order to prevent overfitting and for better learning the fusion features, we design a Modal Feature Erasing (MFE) operation on the multi-modal features which erases features from one randomly selected modality during training. As the result, our approach wins the second place in cvpr 2019 ChaLearn Face Anti-spoofing attack detection challenge. Ourfinal submission gets the score of 99.8052% (TPR@FPR = 10e-4) on the test set.

关键词： Face recognition

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Simultaneous Identification and Tracking of Multiple People using Video and IMUs 32

Simultaneous Identification and Tracking of Multiple People ...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Henschel, Roberto von Marcard, Timo Rosenhahn, Bodo Leibniz Univ Hannover Hannover Germany

ISBN: (纸本)9781728125060

Most modern approaches for multiple people tracking rely on human appearance to exploit similarity between person detections. In this work, we propose an alternative tracking method that does not depend on visual appearance and is still capable to deal with very dynamic motions and long-term occlusions. We make this feasible by: (i) incorporating additional information from body-worn inertial sensors, (ii) designing a neural network to relate person detections to orientation measurements and (iii) formulating a graph labeling problem to obtain a tracking solution that is globally consistent with the video and inertial recordings. We evaluate our approach on several challenging tracking sequences and achieve a very high IDF1 score of 91.2%. We outperform appearance-based baselines in scenarios where appearance is less informative and are on-par in situations with discriminative people appearance.

关键词： computer vision

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GFrames: Gradient-Based Local Reference Frame for 3D Shape Matching 32

GFrames: Gradient-Based Local Reference Frame for 3D Shape M...

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

作者： Melzi, Simone Spezialetti, Riccardo Tombari, Federico Bronstein, Michael M. Di Stefano, Luigi Rodola, Emanuele Univ Verona Verona Italy Univ Bologna Bologna Italy Tech Univ Munich Munich Germany Imperial Coll London USI London England Sapienza Univ Rome Rome Italy

ISBN: (纸本)9781728132938

We introduce GFrames, a novel local reference frame (LRF) construction for 3D meshes and point clouds. GFrames are based on the computation of the intrinsic gradient of a scalar field defined on top of the input shape. The resulting tangent vector field defines a repeatable tangent direction of the local frame at each point;importantly, it directly inherits the properties and invariance classes of the underlying scalar function, making it remarkably robust under strong sampling artifacts, vertex noise, as well as non-rigid deformations. Existing local descriptors can directly benefit from our repeatable frames, as we showcase in a selection of 3D vision and shape analysis applications where we demonstrate state-of-the-art performance in a variety of challenging settings.

关键词： 3D from Multiview and Sensors Categorization Low-level vision Motion and Tracking recognition: Detection Retrieval

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