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End-to-End High-Risk Tackle Detection System for Rugby

End-to-End High-Risk Tackle Detection System for Rugby

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

作者： Nonaka, Naoki Fujihira, Ryo Nishio, Monami Murakami, Hidetaka Tajima, Takuya Yamada, Mutsuo Maeda, Akira Seita, Jun RIKEN Informat R&D & Strategy Headquarters Adv Data Sci Project Tokyo Japan Murakami Surg Hosp Nagasaki Japan Univ Miyazaki Fac Med Miyazaki Japan Ryutsu Keizai Univ Fac Hlth & Sport Sci Ryugasaki Japan Hakata Knee & Sports Clin Fukuoka Japan Kurume Univ Fac Human Hlth Kurume Fukuoka Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Reducing risk of severe injury such as concussion is a high priority for any contact sports. In rugby, Head Injury Assessment (HIA) protocol has been introduced to identify and protect players showing symptoms of concussion and having potential risk of concussion. However, on-field decisions by officials are sometimes difficult and subjective, and HIA is affordable only for elite leagues since it requires medical specialists. To make rugby matches more safe, we aim to develop a system to detect high-risk tackles, potential triggers of concussion, based on deep learning models. Our system takes rugby match video, then first identifies frame with tackle, subsequently detects location of tackle and estimate pose of the ball carrier and the tackler, and finally evaluate the risk of tackle using posture pair of players. Among the model combinations we have examined, the best performance was achieved with the combination of ResNet (2+1)D as tackle frame selection model, RetinaNet as tackle detection model and CenterTrack as pose estimation model. Evaluation using test data, a set of short clips from broadcasted rugby match videos, showed our system was able to detect 50% of high-risk tackles without any human intervention. This result opens a path for automated systems to detect high-risk events, leading to less expensive and more objective monitoring not only for rugby but also for any contact sports.

关键词： Deep learning computer vision Protocols Head conferences Pose estimation pattern recognition

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Learning Co-segmentation by Segment Swapping for Retrieval and Discovery

Learning Co-segmentation by Segment Swapping for Retrieval a...

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

作者： Shen, Xi Efros, Alexei A. Joulin, Armand Aubry, Mathieu Ecole Ponts ParisTech LIGM UMR 8049 Champs Sur Marne France Univ Calif Berkeley Berkeley CA 94720 USA Facebook AI Res Menlo Pk CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The goal of this work is to efficiently identify visually similar patterns in images, e.g. identifying an artwork detail copied between an engraving and an oil painting, or recognizing parts a night-time photograph visible in its daytime counterpart. Lack of training data is a key challenge for this co-segmentation task. We present a simple yet surprisingly effective approach to overcome this difficulty: we generate synthetic training pairs by selecting segments in an image and copy-pasting them into another image. We then learn to predict the repeated region masks. We find that it is crucial to predict the correspondences as an auxiliary task and to use Poisson blending and style transfer on the training pairs to generalize on real data. We analyse results with two deep architectures relevant to our joint image analysis task: a transformer-based architecture and Sparse Nc-Net, a recent network designed to predict coarse correspondences using 4D convolutions. We show our approach provides clear improvements for artwork details retrieval on the Brueghel dataset and achieves competitive performance on two place recognition benchmarks, Tokyo247 and Pitts30K. We also demonstrate the potential of our approach for unsupervised image collection analysis by introducing a spectral graph clustering approach to object discovery and demonstrating it on the object discovery dataset of [49] and the Brueghel dataset. Our code and data are available at http://***/similar to shenx/SegSwap/.

关键词： Training Image segmentation Oils Training data computer architecture Oil insulation Transformers

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A Coarse-to-Fine Boundary Localization method for Naturalistic Driving Action recognition

A Coarse-to-Fine Boundary Localization method for Naturalist...

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

作者： Ding, Guanchen Han, Wenwei Wang, Chenglong Cui, Mingpeng Zhou, Lin Pan, Dianbo Wang, Jiayi Zhang, Junxi Chen, Zhenzhong Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ Sch Resource & Environm Sci Wuhan Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Naturalistic driving action recognition plays an important role in understanding drivers' distracted behaviors in the traffic environment. The main challenge of this task is the accurate localization of the temporal boundary for each distracted driving behavior in the video. Although many temporal action localization methods can identify action categories, it is difficult to predict accurate temporal boundaries for this task since the driving actions of the same category usually present large intra-class variation. In this paper, we introduce a Coarse-to-Fine Boundary Localization method called CFBL, which obtains fine-grained temporal boundaries progressively through three stages. Concretely, in the first coarse boundary generation stage, we adopt a modified anchor-free model Anchor-Free Saliency-based Detector (AFSD) to make an interval estimation of the temporal boundaries of distracted behaviors. In the second boundary refinement stage, we use the Dense Boundary Generation (DBG) model to adjust the estimated interval of the temporal boundaries. In the final boundary decision stage, we build a Localization Boundary Refinement Module to determine the final boundaries of different actions. Besides, we adopt a voting strategy to combine the results of different camera views to enhance the model's distracted driving action classification ability. The experiments conducted on the Track 3 validation set of the 2022 AI City Challenge demonstrate competitive performance of the proposed method.

关键词： Location awareness conferences Urban areas Estimation Detectors Cameras Behavioral sciences

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Deep Scale-space Mining Network for Single Image Deraining

Deep Scale-space Mining Network for Single Image Deraining

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

作者： Li, Pengpeng Jin, Jiyu Jin, Guiyue Fan, Lei Gao, Xiao Song, Tianyu Chen, Xiang Dalian Polytech Univ Sch Informat Sci & Engn Dalian Peoples R China Shenyang Aerosp Univ Coll Elect & Informat Engn Shenyang Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Images captured by outdoor vision systems can often be affected by rain weather, resulting in severe degradation of the visual quality of the captured images. Therefore, image deraining has attracted attention as urgent and challenging research. Many current data-driven approaches achieve better performance but are limited in recovering image details. This is because these methods do not fully mine the correlation of scale-space, which are beneficial for rain removal. In this paper, we design an end-to-end Deep Scalespace Mining Network (DSM-Net) for single image deraining to solve these problems. The proposed network with multi-scale extraction, concurrent attention distillation, and hierarchical information fusion accurately captures scale-space features and learns richer information for better deraining. For better feature extraction, a Multi-scale Attention Block (MAB) is introduced to obtain multi-scale rain streak features by different dilated convolutions. Besides,a Concurrent Attention Distillation Block (CADB) is developed which combined channel attention and subspace attention to calibrate the image features obtained from multiscale acquisition and hierarchical learning, then eliminate redundant features. Importantly, the overall architecture of DSM-Net is inspired by the HourglassNet and DenseNet, which progressively explores and fuses local and global features at different scales in a hierarchical manner instead of direct concatenation. Extensive experiments on synthetic and real datasets show that the proposed DSM-Net outperforms recent state-of-the-art deraining algorithms in terms of both performance and preservation of image details.

关键词： Degradation Deep learning Visualization Rain Correlation Fuses Machine vision

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OMG: Observe Multiple Granularities for Natural Language-Based Vehicle Retrieval

OMG: Observe Multiple Granularities for Natural Language-Bas...

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

作者： Du, Yunhao Zhang, Binyu Ruan, Xiangning Su, Fei Zhao, Zhicheng Chen, Hong Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China China Mobile Res Inst Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Retrieving tracked-vehicles by natural language descriptions plays a critical role in smart city construction. It aims to find the best match for the given texts from a set of tracked vehicles in surveillance videos. Existing works generally solve it by a dual-stream framework, which consists of a text encoder, a visual encoder and a cross-modal loss function. Although some progress has been made, they failed to fully exploit the information at various levels of granularity. To tackle this issue, we propose a novel framework for the natural language-based vehicle retrieval task, OMG, which Observes Multiple Granularities with respect to visual representation, textual representation and objective functions. For the visual representation, target features, context features and motion features are encoded separately. For the textual representation, one global embedding, three local embeddings and a color-type prompt embedding are extracted to represent various granularities of semantic features. Finally, the overall framework is optimized by a cross-modal multi-granularity contrastive loss function. Experiments demonstrate the effectiveness of our method. Our OMG significantly outperforms all previous methods and ranks the 9th on the 6th AI City Challenge Track2.

关键词： Representation learning Visualization Smart cities Surveillance Semantics Feature extraction pattern recognition

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Nighttime Image Dehazing Based on Variational Decomposition Model

Nighttime Image Dehazing Based on Variational Decomposition ...

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

作者： Liu, Yun Yan, Zhongsheng Wu, Aimin Ye, Tian Li, Yuche Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Chongqing Coll Int Business & Econ Coll Big Data & Intelligent Engn Chongqing 401520 Peoples R China Jimei Univ Sch Ocean Informat Engn Xiamen 361021 Peoples R China China Univ Petr Coll Geosci Beijing 102249 Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Most of existing dehazing algorithms are unable to deal with nighttime hazy scenarios well due to complex degraded factors such as non-uniform illumination, low light and glows. To obtain high-quality image under nighttime haze imaging conditions, we present an effective single nighttime image dehazing framework based on a variational decomposition model to simultaneously address these undesirable issues. First, a variational decomposition model consisting of three regularization terms is proposed to simultaneously decompose a nighttime hazy image into a structure layer, a detail layer and a noise layer. Concretely, we employ l(1) norm to constrain the structure component, adopt l(0) sparsity term to enforce the piece-wise continuous of the detail layer, and use l(2) norm to separate the noise layer. Next, the structure layer is recovered by means of inversing the physical model and the detail layers are revealed in a multi-scale gradient enhancement manner. Finally, the dehazed structure layer and the enhanced detail layers are integrated into a haze-free image. Experimental results show that the proposed framework achieves superior performance on nighttime haze removal and noise suppression compared with several state-of-the-art dehazing techniques.

关键词： Degradation computer vision conferences Computational modeling Noise reduction Lighting Focusing

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An Ensemble Approach for Facial Behavior Analysis in-the-wild Video

An Ensemble Approach for Facial Behavior Analysis in-the-wil...

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

作者： Hong-Hai Nguyen Van-Thong Huynh Kim, Soo-Hyung Chonnam Natl Univ Dept Artificial Intelligence Convergence Gwangju South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human emotions recognization contributes to the development of human-computer interaction. The machines understanding human emotions in the real world will significantly contribute to life in the future. This paper introduces the 3rd Affective Behavior Analysis in-the-wild (ABAW3) 2022 challenge. We focused on solving the problem of the Valence-Arousal (VA) estimation and Action Unit (AU) detection. For valence-arousal estimation, we conducted two stages: creating new features from multimodel and temporal learning to predict valence-arousal. First, we make new features;the Gated Recurrent Unit (GRU) and Transformer are combined using a Regular Networks (RegNet) feature, which is extracted from the image. The next step is the GRU combined with local attention to predict valence-arousal. The Concordance Correlation Coefficient (CCC) was used to evaluate the model. The result achieved 0.450 for valence and 0.445 for arousal on the test set, outperforming the baseline method with a corresponding CCC of 0.180 for valence and 0.170 for arousal. We also performed additional experiments on the action unit task with simple transformer blocks. We achieved a score of 49.04 on the test set in terms of F-1 score, which outperforms the baseline method with a corresponding F1 score of 36.50. Our submission to ABAW3 2022 ranks 3rd for both tasks.

关键词： Human computer interaction Sentiment analysis Gold Estimation Logic gates Feature extraction Transformers

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FenceNet: Fine-grained Footwork recognition in Fencing

FenceNet: Fine-grained Footwork Recognition in Fencing

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

作者： Zhu, Kevin Wong, Alexander McPhee, John Univ Waterloo Waterloo ON Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Current data analysis for the Canadian Olympic fencing team is primarily done manually by coaches and analysts. Due to the highly repetitive, yet dynamic and subtle movements in fencing, manual data analysis can be inefficient and inaccurate. We propose FenceNet as a novel architecture to automate the classification of fine-grained footwork techniques in fencing. FenceNet takes 2D pose data as input and classifies actions using a skeleton-based action recognition approach that incorporates temporal convolutional networks to capture temporal information. We train and evaluate FenceNet on the Fencing Footwork Dataset (FFD), which contains 10 fencers performing 6 different footwork actions for 10-11 repetitions each (652 total videos). FenceNet achieves 85.4% accuracy under 10-fold cross-validation, where each fencer is left out as the test set. This accuracy is within 1% of the current state-of-the-art method, JLJA (86.3%), which selects and fuses features engineered from skeleton data, depth videos, and inertial measurement units. BiFenceNet, a variant of FenceNet that captures the "bidirectionality" of human movement through two separate networks, achieves 87.6% accuracy, outperforming JLJA. Since neither FenceNet nor BiFenceNet requires data from wearable sensors, unlike JLJA, they could be directly applied to most fencing videos, using 2D pose data as input extracted from off-the-shelf 2D human pose estimators. In comparison to JLJA, our methods are also simpler as they do not require manual feature engineering, selection, or fusion.

关键词： Data analysis Measurement units Limiting Fuses Manuals Sensor fusion Skeleton

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Generalizing Adversarial Explanations with Grad-CAM

Generalizing Adversarial Explanations with Grad-CAM

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

作者： Chakraborty, Tanmay Trehan, Utkarsh Mallat, Khawla Dugelay, Jean-Luc EURECOM Campus SophiaTech450 Route Chappes F-06410 Biot France SAP Secur Res Labs France Biot France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Gradient-weighted Class Activation Mapping (Grad-CAM), is an example-based explanation method that provides a gradient activation heat map as an explanation for Convolution Neural Network (CNN) models. The drawback of this method is that it cannot be used to generalize CNN behaviour. In this paper, we present a novel method that extends Grad-CAM from example-based explanations to a method for explaining global model behaviour. This is achieved by introducing two new metrics, (i) Mean Observed Dissimilarity (MOD) and (ii) Variation in Dissimilarity (VID), for model generalization. These metrics are computed by comparing a Normalized Inverted Structural Similarity Index (NISSIM) metric of the Grad-CAM generated heatmap for samples from the original test set and samples from the adversarial test set. For our experiment, we study adversarial attacks on deep models such as VGG16, ResNet50, and ResNet101, and wide models such as InceptionNetv3 and XceptionNet using Fast Gradient Sign Method (FGSM). We then compute the metrics MOD and VID for the automatic face recognition (AFR) use case with the VGGFace2 dataset. We observe a consistent shift in the region highlighted in the Grad-CAM heatmap, reflecting its participation to the decision making, across all models under adversarial attacks. The proposed method can be used to understand adversarial attacks and explain the behaviour of black box CNN models for image analysis.

关键词： Measurement Heating systems Image analysis Computational modeling Face recognition Neural networks Decision making

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CIPPSRNet: A Camera Internal Parameters Perception Network Based Contrastive Learning for Thermal Image Super-Resolution

CIPPSRNet: A Camera Internal Parameters Perception Network B...

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

作者： Wang, Kai Sun, Qigong Wang, Yicheng Wei, Huiyuan Lv, Chonghua Tian, Xiaolin Liu, Xu SenseTime Res Hong Kong Peoples R China Xidian Univ Xian Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Thermal Image Super-Resolution (TISR) is a technique for converting Low-Resolution (LR) thermal images to High-Resolution (HR) thermal images. This technique has recently become a research hotspot due to its ability to reduce sensor costs and improve visual perception. However, current research does not provide an effective solution for multi-sensor data training, possibly driven by pixel mismatch and simple degradation setting issues. In this paper, we proposed a Camera Internal Parameters Perception Network (CIPPSRNet) for LR thermal image enhancement. The camera internal parameters (CIP) were explicitly modeled as a feature representation, the LR features were transformed into the intermediate domain containing the internal parameters information by perceiving CIP representation. The mapping between the intermediate domain and the spatial domain of the HR features was learned by CIPPSRNet. In addition, we introduced contrastive learning to optimize the pretrained Camera Internal Parameters Representation Network and the feature encoders. Our proposed network is capable of achieving a more efficient transformation from the LR to the HR domains. Additionally, the use of contrastive learning can improve the network's adaptability to misalignment data with insufficient pixel matching and its robustness. Experiments on PBVS2022 TISR Dataset show that our network has achieved state-of-the-art performance for the Thermal SR task.

关键词： Training Degradation Visualization Superresolution Cameras Robustness pattern recognition

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