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2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

作者： Chaudhary, Sachin Murala, Subrahmanyam Computer Vision and Pattern Recognition Lab Depatment of Electrical Engineering Indian Institute of Technology Ropar India

ISBN: (纸本)9781538666500

The all-weather intelligent surveillance system is the prime challenge for computer vision researchers. The surveillance is mostly done to analyze the human activity in a particular region. Several extreme weather conditions like rain, snow, haze, fog etc. halts the surveillance process and thus decreases the reliability of the surveillance system. Here, an attempt is made to tackle one of these weather situation i.e. haze in case of surveillance. Haze distorts the quality of images and videos captured by camera. Due to poor quality, it is difficult to analyze the haze degraded video for the human activities using the existing state-of-the-art methods for human action recognition (HAR). Therefore, in this paper, a new two level saliency based end-to-end network (TSNet) for HAR in hazy videos is proposed. De-hazing approaches given in [1]-[9] have certain limitations and therefore we fine-tuned the de-hazing network given in [10] for HAR. The concept of rank pooling given in [11] is further utilized to efficiently represent the temporal saliency of the video. The transmission map information is utilized here to fix the spatial saliency in each frame. As currently, there is no dataset availab.e for HAR in hazy video, here a new dataset of hazy video is generated from two benchmark datasets namely HMDB51 [12] and UCF101 [13] by adding synthetic haze. The existing methods for HAR proposed in [11], [14], [15] are applied and compared with the proposed method on proposed hazy-HMDB51 and hazy-UCF101. The proposed method clearly outperforms the above mentioned methods in terms of average recognition rate (ARR). © 2018 IEEE.

关键词： Monitoring

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Adaptive Pyramid Context Network for Semantic Segmentation

Adaptive Pyramid Context Network for Semantic Segmentation

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IEEE/CVF Conference on computer vision and pattern recognition

作者： Junjun He Zhongying Deng Lei Zhou Yali Wang Yu Qiao Shenzhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

ISBN: (纸本)9781728132945

Recent studies witnessed that context features can significantly improve the performance of deep semantic segmentation networks. Current context based segmentation methods differ with each other in how to construct context features and perform differently in practice. This paper firstly introduces three desirable properties of context features in segmentation task. Specially, we find that Global-guided Local Affinity (GLA) can play a vital role in constructing effective context features, while this property has been largely ignored in previous works. Based on this analysis, this paper proposes Adaptive Pyramid Context Network (APCNet) for semantic segmentation. APCNet adaptively constructs multi-scale contextual representations with multiple well-designed Adaptive Context Modules (ACMs). Specifically, each ACM leverages a global image representation as a guidance to estimate the local affinity coefficients for each sub-region, and then calculates a context vector with these affinities. We empirically evaluate our APCNet on three semantic segmentation and scene parsing datasets, including PASCAL VOC2012, Pascal-Context, and ADE20K dataset. Experimental results show that APCNet achieves state-of-the-art performance on all three benchmarks, and obtains a new record 84.2% on PASCAL VOC 2012 test set without MS COCO pre-trained and any post-processing.

关键词： Semantics subregion image representation Pascal TEST SETS

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RankSRGAN: Generative adversarial networks with ranker for image super-resolution

arXiv

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arXiv 2019年

作者： Zhang, Wenlong Liu, Yihao Dong, Chao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences

Generative Adversarial Networks (GAN) have demonstrated the potential to recover realistic details for single image super-resolution (SISR). To further improve the visual quality of super-resolved results, PIRM2018-SR Challenge employed perceptual metrics to assess the perceptual quality, such as PI, NIQE, and Ma. However, existing methods cannot directly optimize these indifferentiable perceptual metrics, which are shown to be highly correlated with human ratings. To address the problem, we propose Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator in the direction of perceptual metrics. Specifically, we first train a Ranker which can learn the behavior of perceptual metrics and then introduce a novel rank-content loss to optimize the perceptual quality. The most appealing part is that the proposed method can combine the strengths of different SR methods to generate better results. Extensive experiments show that RankSRGAN achieves visually pleasing results and reaches state-of-the-art performance in perceptual metrics. Project page: https://***/Projects/RankSRGAN. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Robust text line detection in equipment nameplate images

Robust text line detection in equipment nameplate images

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Lai, Jiangyu Guo, Lanqing Qiao, Yu Chen, Xiaolong Zhang, Zhengfu Liu, Canping Li, Ying Fu, Bin Guangzhou Power Supply Bureau Co. Ltd. Guangzhou China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIATSenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9781728163215

Scene text detection for equipment nameplates in the wild is important for equipment inspection robot since it enables inspection robot to take specific actions for different equipment's. Although text detection in images has achieved great progress in recent years, the detection for equipment nameplates faces several challenges such as extreme illumination and distortion which significantly decrease the detection performance. In this paper, we propose a deep text detection model Robust Text Line Detection (RTLD) for locating word level text instances in equipment cards. Specifically, the proposed model first employs a corner detection module to determine the four corner points of each nameplate, and then a carefully designed image transformed module transforms the irregular nameplate region into a rectangular region. Finally, text detection module is introduced to locate every word level text instance in the transformed images. We conduct extensive experiments to examine our proposed methods on real equipment nameplate images. Our model achieves 91.2% precision and 92.6% recall on Equipment Nameplate Dataset. The experimental results demonstrate the effectiveness of our models. © 2019 IEEE.

关键词： Nameplates

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Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers

Modulating Image Restoration with Continual Levels via Adapt...

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IEEE/CVF Conference on computer vision and pattern recognition

作者： Jingwen He Chao Dong Yu Qiao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

ISBN: (纸本)9781728132945

In image restoration tasks, like denoising and super-resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods. Learning from discrete and fixed restoration levels, deep models cannot be easily generalized to data of continuous and unseen levels. This topic is rarely touched in literature, due to the difficulty of modulating well-trained models with certain hyper-parameters. We make a step forward by proposing a unified CNN framework that consists of little additional parameters than a single-level model yet could handle arbitrary restoration levels between a start and an end level. The additional module, namely AdaFM layer, performs channel-wise feature modification, and can adapt a model to another restoration level with high accuracy. By simply tweaking an interpolation coefficient, the intermediate model - AdaFM-Net could generate smooth and continuous restoration effects without artifacts. Extensive experiments on three image restoration tasks demonstrate the effectiveness of both model training and modulation testing. Besides, we carefully investigate the properties of AdaFM layers, providing a detailed guidance on the usage of the proposed method.

关键词： image restoration technique restoration Noise reduction

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Age estimation using disconnectedness features in handwriting 15

Age estimation using disconnectedness features in handwritin...

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15th IAPR International Conference on Document Analysis and recognition, ICDAR 2019

作者： Basavaraja, V. Shivakumara, Palaiahnakote Guru, D.S. Pal, Umapada Lu, Tong Blumenstein, Michael Department of Studies in Computer Science University of Mysore Mysore Karnataka India Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China University of Technology Sydney Australia

ISBN: (纸本)9781728128610

Real-time applications of handwriting analysis have increased drastically in the fields of forensic and information security because of accurate cues. One of such applications is human age estimation based on handwriting for the purpose of immigrant checking. In this paper, we have proposed a new method for age estimation using handwriting analysis using Hu invariant moments and disconnectedness features. To make the proposed method robust to both ruled and un-ruled documents, we propose to explore intersection point detection in Canny edge images of each input document, which results in text components. For each text component pair, we propose Hu invariant moments for extracting disconnectedness features, which in fact measure multi-shape components based on distance, shape and mutual position analysis of components. Furthermore, iterative k-means clustering is proposed for the classification of different age groups. Experimental results on our dataset and some standard datasets, namely, IAM and KHATT, show that the proposed method is effective and outperforms the state-of-the-art methods. © 2019 IEEE.

关键词： Security of data

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A new unified method for detecting text from marathon runners and sports players in video

arXiv

引用

arXiv 2020年

作者： Nag, Sauradip Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Blumenstein, Michael Kalyani Government Engineering College Kalyani Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Sydney Australia

Detecting text located on the torsos of marathon runners and sports players in video is a challenging issue due to poor quality and adverse effects caused by flexible/colorful clothing, and different structures of human bodies or actions. This paper presents a new unified method for tackling the above challenges. The proposed method fuses gradient magnitude and direction coherence of text pixels in a new way for detecting candidate regions. Candidate regions are used for determining the number of temporal frame clusters obtained by K-means clustering on frame differences. This process in turn detects key frames. The proposed method explores Bayesian probability for skin portions using color values at both pixel and component levels of temporal frames, which provides fused images with skin components. Based on skin information, the proposed method then detects faces and torsos by finding structural and spatial coherences between them. We further propose adaptive pixels linking a deep learning model for text detection from torso regions. The proposed method is tested on our own dataset collected from marathon/sports video and three standard datasets, namely, RBNR, MMM and R-ID of marathon images, to evaluate the performance. In addition, the proposed method is also tested on the standard natural scene datasets, namely, CTW1500 and MS-COCO text datasets, to show the objectiveness of the proposed method. A comparative study with the state-of-the-art methods on bib number/text detection of different datasets shows that the proposed method outperforms the existing methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Pixels

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MetaCleaner: Learning to Hallucinate Clean Representations for Noisy-lab.led Visual recognition

MetaCleaner: Learning to Hallucinate Clean Representations f...

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IEEE/CVF Conference on computer vision and pattern recognition

作者： Weihe Zhang Yali Wang Yu Qiao Shenzhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

ISBN: (纸本)9781728132945

Deep Neural Networks (DNNs) have achieved remarkable successes in large-scale visual recognition. However, they often suffer from overfitting under noisy lab.ls. To alleviate this problem, we propose a conceptually simple but effective MetaCleaner, which can learn to hallucinate a clean representation of an object category, according to a small noisy subset from the same category. Specially, MetaCleaner consists of two flexible submodules. The first sub-module, namely Noisy Weighting, can estimate the confidence scores of all the images in the noisy subset, by analyzing their deep features jointly. The second submodule, namely Clean Hallucinating, can generate a clean representation from the noisy subset, by summarizing the noisy images with their confidence scores. Via MetaCleaner, DNNs can strengthen its robustness to noisy lab.ls, as well as enhance its generalization capacity with richer data diversity. Moreover, MetaCleaner can be easily integrated into the standard training procedure of DNNs, which promotes its value for real-life applications. We conduct extensive experiments on two popular benchmarks in noisy-lab.led recognition, i.e., Food-101N and Clothing1M. For both datasets, our MetaCleaner significantly outperforms baselines, and achieves the state-of-the-art performance.

关键词： Noise Hallucinations confidence training procedure submodule recognition (Psychology) Dataset

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The equipment nameplate dataset for scene text detection and recognition

The equipment nameplate dataset for scene text detection and...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Chen, Xiaolong Zhang, Zhengfu Qiao, Yu Zhang, Pu Guo, Lanqing Chen, Wenrui Chen, Chen Fu, Bin Guangzhou Power Supply Bureau Co. Ltd. Guangzhou China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9781728163215

In this paper, we introduce the Equipment Nameplate Dataset, a large dataset for scene text detection and recognition. Natural images in this dataset are taken in the wild and thus this dataset includes various intra-class inconsistency such as ill illumination conditions and partly occluded, which makes our dataset more challenging than other datasets. In order to make people train detection and recognition model separately, we annotate our dataset not only word instance, but also text region by using rectangle bounding boxes. Some detailed statistics information about our dataset will be given so that people can use them to analyse and develop their own models. Moreover, we use our dataset to test some famous detection and recognition models and present the corresponding results in order to make researcher compare them with their own models. Dataset will be publicly availab.e on the website. © 2019 IEEE.

关键词： computer vision

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Orientation robust scene text recognition in natural scene

Orientation robust scene text recognition in natural scene

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Chen, Xiaolong Zhang, Zhengfu Qiao, Yu Lai, Jiangyu Jiang, Jian Zhang, Zeyu Fu, Bin Guangzhou Power Supply Bureau Co. Ltd. Guangzhou China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9781728163215

In recent years, scene text recognition has achieved significant improvement and various state-of-the-art recognition approaches have been proposed. This paper focused on recognizing text in natural photos of equipment nameplates, which has wide applications in industrial automations. This task only receives little attentions in previous works. The challenge of this problem comes from multi-orientation, curved, noisy and blurry text patches in equipment nameplates. To address this problem, we propose a deep model for text recognition in multi-oriented nameplates, namely, Orientation Robust Scene Text recognition (ORSTR). Specifically, our model employs a rectification module to transform curved, distorted or multi-orientation text to near-horizontal text with a carefully designed rectification module. Once the near-horizontal text has been generated, recognition network will output the predictions of text patches. Our scene text recognition model achieves 90.8% recognition accuracy on equipment nameplate dataset which outperforms previous scene text recognition model (CRNN) about 0.8%. Several extensive experiments have been conducted to verify the effectiveness of our model. © 2019 IEEE.

关键词： Nameplates

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