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Human object interactions recognition based on social network analysis

Human object interactions recognition based on social networ...

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Applied Imagery Pattern Recognition workshop (AIPR)

作者： Guang Yang Yafeng Yin Hong Man Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA

ISBN: (纸本)9781479925414

Recognizing human-object interactions in videos is a very challenging problem in computer vision research. There are two major difficulties lying in this task: (1) The detection of human body parts and objects is usually affected by the quality of the videos, for instance, low resolutions of the videos, camera motions, and blurring frames caused by fast motions, as well as the self-occlusions during human-object interactions. (2) The spatial and temporal dynamics of human-object interaction are hard to model. In order to overcome those natural obstacles, we propose a new method using social network analysis (SNA) based features to describe the distributions and relationships of low level objects for human-object interaction recognition. In this approach, the detected human body parts and objects are treated as nodes in social network graphs, and a set of SNA features including closeness, centrality and centrality with relative velocity are extracted for action recognition. A major advantage of SNA based feature set is its robustness to varying node numbers and erroneous node detections, which are very common in human-object interactions. An SNA feature vector will be extracted for each frame and different human-object interactions are classified based on these features. Two classification methods, including Support Vector Machine (SVM) and Hidden Markov Model (HMM), have been used to evaluate the proposed feature set on four different human-object interactions from HMDB dataset [1]. The experimental results demonstrated that the proposed framework can effectively capture the dynamical characteristics of human-object interaction and outperforms the state of art methods in human-object interaction recognition.

关键词： Social network services Videos Hidden Markov models Feature extraction Vectors Support vector machines Trajectory

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X-SAR SpotLigh images feature selection and water segmentation

X-SAR SpotLigh images feature selection and water segmentati...

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ieee International workshop on Imaging Systems and Techniques (IST)

作者： Bruno Cafaro Silvia Canale Fiora Pirri Department of Computer Control and Management Engineering |A. Ruberti| Sapienza Università di Roma Roma Italy

In this paper we address the feature selection problem for X-SAR images and further the segmentation of specific chosen classes. After defining a suitable feature space for X-SAR images we select the most significant ones via a supervised machine learning approach: the 1-norm SVM. The selected features will be used for segmentation purposes, in order to segment water areas from the background. We shall see that the most relevant features are based on texture elements. So the segmentation is texture based and achieved with variational calculus and level set methods. The work is mainly focused on urban park X-SAR SpotLight images, where lakes and rivers are often present. The images are collected with the COSMO-SkyMed satellites constellation, equipped with a SAR sensor.

关键词： Image segmentation Equations Mathematical model Image resolution Support vector machines Synthetic aperture radar Joints

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Adversarial Defense based on Structure-to-Signal Autoencoders

Adversarial Defense based on Structure-to-Signal Autoencoder...

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ieee workshop on Applications of computer vision (WACV)

作者： Joachim Folz Sebastian Palacio Joern Hees Andreas Dengel German Research Center for Artificial Intelligence (DFKI) TU Kaiserslautern

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

Adversarial attacks have exposed the intricacies of the complex loss surfaces approximated by neural networks. In this paper, we present a defense strategy against gradient-based attacks, on the premise that input gradients need to expose information about the semantic manifold for attacks to be successful. We propose an architecture based on compressive autoencoders (AEs) with a two-stage training scheme, creating not only an architectural bottleneck but also a representational bottleneck. We show that the proposed mechanism yields robust results against a collection of gradient-based attacks under challenging white-box conditions. This defense is attack-agnostic and can, therefore, be used for arbitrary pre-trained models, while not compromising the original performance. These claims are supported by experiments conducted with state-of-the-art image classifiers (ResNet50 and Inception v3), on the full ImageNet validation set. Experiments, including counterfactual analysis, empirically show that the robustness stems from a shift in the distribution of input gradients, which mitigates the effect of tested adversarial attack methods. Gradients propagated through the proposed AEs represent less semantic information and instead point to low-level structural features.

关键词： Perturbation methods Semantics Robustness Predictive models Training Decoding Neural networks

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variational image segmentation models: Application to medical images MRI

Variational image segmentation models: Application to medica...

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ieee International Conference on Multimedia Computing and Systems (ICMCS)

作者： Samir Bara Mounir Ait Kerroum Ahmed Hammouch Driss Aboutajdine UFR LRIT Faculty of Sciences Mohamed V-Agdal University Rabat Morocco Equipe Imagerie et Multimedia Laboratoire LARIT ENCG Université Ibn Tofail Kenitra Morocco Laboratory LRGE ENSET Mohammed-V University Rabat Morocco

Image segmentation is an important branch of computer vision. Its aim is to extract meaningful lying in objects images, either by dividing images into contiguous semantic regions, or by extracting one or several objects more specific in images, such as medical structures. In general, image segmentation task is very difficult to achieve it since natural images are diverse, complex and the way we perceive them, vary according to individuals. More than a decade ago, a promising mathematical framework, based on variational models and partial differential equations, have been investigated to solve the image segmentation problem. This new approach benefits from well-established mathematical theories that allow people to analyze, understand and extend segmentation methods. Moreover, this framework is defined in a continuous setting which makes the proposed models independent with respect to the grid of digital images.

关键词： Image segmentation level set Mathematical model Biomedical imaging Active contours Pixel Noise

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 ieee/CVF Conference on computer vision and Pattern Recognition workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 ieee.

关键词： Data acquisition

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RPM-Net: Robust Pixel-level Matching Networks for Self-Supervised Video Object Segmentation

RPM-Net: Robust Pixel-Level Matching Networks for Self-Super...

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ieee workshop on Applications of computer vision (WACV)

作者： Youngeun Kim Seokeon Choi Hankyeol Lee Taekyung Kim Changick Kim KAIST

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

In this paper, we introduce a self-supervised approach for video object segmentation without human labeled data. Specifically, we present Robust Pixel-level Matching Networks (RPM-Net), a novel deep architecture that matches pixels between adjacent frames, using only color information from unlabeled videos for training. Technically, RPM-Net can be separated in two main modules. The embedding module first projects input images into high dimensional embedding space. Then the matching module with deformable convolution layers matches pixels between reference and target frames based on the embedding features. Unlike previous methods using deformable convolution, our matching module adopts deformable convolution to focus on similar features in spatio-temporally neighboring pixels. Our experiments show that the selective feature sampling improves the robustness to challenging problems in video object segmentation such as camera shake, fast motion, deformation, and occlusion. Also, we carry out comprehensive experiments on three public datasets (i.e., DAVIS-2017, SegTrack-v2, and Youtube-Objects) and achieve state-of-the-art performance on self-supervised video object segmentation. Moreover, we significantly reduce the performance gap between self-supervised and fully-supervised video object segmentation (41.0% vs. 52.5% on DAVIS-2017 validation set).

关键词： Convolution Training Object segmentation Feature extraction Robustness Image segmentation Image color analysis

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Generating Positive Bounding Boxes for Balanced Training of Object Detectors

Generating Positive Bounding Boxes for Balanced Training of ...

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ieee workshop on Applications of computer vision (WACV)

作者： Kemal Oksuz Baris Can Cam Emre Akbas Sinan Kalkan Department of Computer Engineering Middle East Technical University Ankara Turkey

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

Two-stage deep object detectors generate a set of regions-of-interest RoIs in the first stage, then, in the second stage, identify objects among the proposed RoIs that sufficiently overlap with a ground truth (GT) box. The second stage is known to suffer from a bias towards RoIs that have low intersection-over-union (IoU) with the associated GT boxes. To address this issue, we first propose a sampling method to generate bounding boxes (BB) that overlap with a given reference box more than a given IoU threshold. Then, we use this BB generation method to develop a positive RoI (pRoI) generator that, for the second stage, produces RoIs following any desired spatial or IoU distribution. We show that our pRoI generator is able to simulate other sampling methods for positive examples such as hard example mining and prime sampling. Using our generator as an analysis tool, we show that (i) IoU imbalance has an adverse effect on performance, (ii) hard positive example mining improves the performance only for certain input IoU distributions, and (iii) the imbalance among the foreground classes has an adverse effect on performance and that it can be alleviated at the batch level. Finally, we train Faster R-CNN using our pRoI generator and, compared to conventional training, obtain better or on-par performance for low IoUs and significant improvements when trained for higher IoUs for Pascal VOC and MS COCO datasets. The code is available at: https://***/kemaloksuz/BoundingBoxGenerator.

关键词： Generators Detectors Training Object detection Sampling methods Pipelines Proposals

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MotionRec: A Unified Deep Framework for Moving Object Recognition

MotionRec: A Unified Deep Framework for Moving Object Recogn...

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ieee workshop on Applications of computer vision (WACV)

作者： Murari Mandal Lav Kush Kumar Mahipal Singh Saran Santosh Kumar Vipparthi Vision Intelligence Lab Malaviya National Institute of Technology Jaipur India

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

In this paper we present a novel deep learning framework to perform online moving object recognition (MOR) in streaming videos. The existing methods for moving object detection (MOD) only computes class-agnostic pixel-wise binary segmentation of video frames. On the other hand, the object detection techniques do not differentiate between static and moving objects. To the best of our knowledge, this is a first attempt for simultaneous localization and classification of moving objects in a video, i.e. MOR in a single-stage deep learning framework. We achieve this by labelling axis-aligned bounding boxes for moving objects which requires less computational resources than producing pixel-level estimates. In the proposed MotionRec, both temporal and spatial features are learned using past history and current frames respectively. First, the background is estimated with a temporal depth reductionist (TDR) block. Then the estimated background, current frame and temporal median of recent observations are assimilated to encode spatiotemporal motion saliency. Moreover, feature pyramids are generated from these motion saliency maps to perform regression and classification at multiple levels of feature abstractions. MotionRec works online at inference as it requires only few past frames for MOR. Moreover, it doesn't require predefined target initialization from user. We also annotated axis-aligned bounding boxes (42,614 objects (14,814 cars and 27,800 person) in 24,923 video frames of CDnet 2014 dataset) due to lack of available benchmark datasets for MOR. The performance is observed qualitatively and quantitatively in terms of mAP over a defined unseen test set. Experiments show that the proposed MotionRec significantly improves over strong baselines with RetinaNet architectures for MOR.

关键词： Feature extraction Videos Object detection Detectors History Estimation Task analysis

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AIM 2019 Challenge on Real-World Image Super-Resolution: methods and Results

AIM 2019 Challenge on Real-World Image Super-Resolution: Met...

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International Conference on computer vision workshops (ICCV workshops)

作者： Andreas Lugmayr Martin Danelljan Radu Timofte Manuel Fritsche Shuhang Gu Kuldeep Purohit Praveen Kandula Maitreya Suin Rajagoapalan A. N. Nam Hyung Joon Yu Seung Won Guisik Kim Dokyeong Kwon Chih-Chung Hsu Chia-Hsiang Lin Yuanfei Huang Xiaopeng Sun Wen Lu Jie Li Xinbo Gao Sefi Bell-Kligler Assaf Shocher Michal Irani ETH Zurich No Affiliation

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided in the challenge. In Track 1: Source Domain the aim is to super-resolve such images while preserving the low level image characteristics of the source input domain. In Track 2: Target Domain a set of high-quality images is also provided for training, that defines the output domain and desired quality of the super-resolved images. To allow for quantitative evaluation, the source input images in both tracks are constructed using artificial, but realistic, image degradations. The challenge is the first of its kind, aiming to advance the state-of-the-art and provide a standard benchmark for this newly emerging task. In total 7 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem.

关键词： Image resolution Degradation Training Target tracking Benchmark testing Artificial neural networks Task analysis

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