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FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

FishEye8K: A Benchmark and Dataset for Fisheye Camera Object...

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

作者： Munkhjargal Gochoo Munkh-Erdene Otgonbold Erkhembayar Ganbold Jun-Wei Hsieh Ming-Ching Chang Ping-Yang Chen Byambaa Dorj Hamad Al Jassmi Ganzorig Batnasan Fady Alnajjar Mohammed Abduljabbar Fang-Pang Lin Department of Computer Science and Software Engineering United Arab Emirates University UAE Emirates Center for Mobility Research United Arab Emirates University UAE College of AI and Green Energy National Yang Ming Chiao Tung University Taiwan University at Albany — State University of New York NY USA Department of Computer Science National Yang Ming Chiao Tung University Taiwan Mongolian University of Science and Technology Mongolia National Center for High-Performance Computing Taiwan

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080×1080 and 1280×1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640×640 and 1280×1280, respectively. The dataset will be available on the GitHub link with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications.

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Imaging Signal Recovery Using Neural Network Priors Under Uncertain Forward Model Parameters

Imaging Signal Recovery Using Neural Network Priors Under Un...

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

作者： Xiwen Chen Wenhui Zhu Peijie Qiu Abolfazl Razi School of Computing Clemson University School of Computing and Augmented Intelligence Arizona State University McKeley School of Engineering Washington University in St. Louis

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Inverse imaging problems (IIPs) arise in various applications, with the main objective of reconstructing an image from its compressed measurements. This problem is often ill-posed for being under-determined with multiple interchangeably consistent solutions. The best solution inherently depends on prior knowledge or assumptions, such as the sparsity of the image. Furthermore, the reconstruction process for most IIPs relies significantly on the imaging (i.e. forward model) parameters, which might not be fully known, or the measurement device may undergo calibration drifts. These uncertainties in the forward model create substantial challenges, where inaccurate reconstructions usually happen when the postulated parameters of the forward model do not fully match the actual ones. In this work, we devoted to tackling accurate reconstruction under the context of a set of possible forward model parameters that exist. Here, we propose a novel Moment-Aggregation (MA) framework that is compatible with the popular IIP solution by using a neural network prior. Specifically, our method can reconstruct the signal by considering all candidate parameters of the forward model simultaneously during the update of the neural network. We theoretically demonstrate the convergence of the MA framework, which has a similar complexity with reconstruction under the known forward model parameters. Proof-of-concept experiments demonstrate that the proposed MA achieves performance comparable to the forward model with the known precise parameter in reconstruction across both compressive sensing and phase retrieval applications, with a PSNR gap of 0.17 to 1.94 over various datasets, including MNIST, X-ray, Glas, and MoNuseg. This highlights our method’s significant potential in reconstruction under an uncertain forward model.

关键词： Uncertainty Image coding Computational modeling Neural networks Imaging pattern recognition Image reconstruction

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The 8th AI City Challenge

The 8th AI City Challenge

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

作者： Shuo Wang David C. Anastasiu Zheng Tang Ming-Ching Chang Yue Yao Liang Zheng Mohammed Shaiqur Rahman Meenakshi S. Arya Anuj Sharma Pranamesh Chakraborty Sanjita Prajapati Quan Kong Norimasa Kobori Munkhjargal Gochoo Munkh-Erdene Otgonbold Fady Alnajjar Ganzorig Batnasan Ping-Yang Chen Jun-Wei Hsieh Xunlei Wu Sameer Satish Pusegaonkar Yizhou Wang Sujit Biswas Rama Chellappa NVIDIA Corporation CA USA Santa Clara University CA USA University at Albany SUNY NY USA Australian National University Australia Iowa State University IA USA Indian Institute of Technology Kanpur India Woven by Toyota Japan United Arab Emirates University UAE Emirates Center for Mobility Research UAE National Yang-Ming Chiao-Tung University Taiwan Johns Hopkins University MD USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities. The 2024 edition featured five tracks, attracting unprecedented interest from 726 teams in 47 countries and regions. Track 1 dealt with multi-target multi-camera (MTMC) people tracking, highlighting significant enhancements in camera count, character number, 3D annotation, and camera matrices, alongside new rules for 3D tracking and online tracking algorithm encouragement. Track 2 introduced dense video captioning for traffic safety, focusing on pedestrian accidents using multi-camera feeds to improve insights for insurance and prevention. Track 3 required teams to classify driver actions in a naturalistic driving analysis. Track 4 explored fish-eye camera analytics using the FishEye8K dataset. Track 5 focused on motorcycle helmet rule violation detection. The challenge utilized two leaderboards to showcase methods, with participants setting new benchmarks, some surpassing existing state-of-the-art achievements.

关键词： computer vision Three-dimensional displays Pedestrians Prevention and mitigation Urban areas Cameras Safety

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Temporal Query Networks for Fine-grained Video Understanding

Temporal Query Networks for Fine-grained Video Understanding

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

作者： Zhang, Chuhan Gupta, Ankush Zisserman, Andrew Univ Oxford Oxford England DeepMind London England

ISBN: (纸本)9781665445092

Our objective in this work is fine-grained classification of actions in untrimmed videos, where the actions may be temporally extended or may span only a few frames of the video. We cast this into a query-response mechanism, where each query addresses a particular question, and has its own response label set. We make the following four contributions: (i) We propose a new model-a Temporal Query Network-which enables the query-response functionality, and a structural understanding of fine-grained actions. It attends to relevant segments for each query with a temporal attention mechanism, and can be trained using only the labels for each query. (ii) We propose a new way-stochastic feature bank update-to train a network on videos of various lengths with the dense sampling required to respond to fine-grained queries. (iii) we compare the TQN to other architectures and text supervision methods, and analyze their pros and cons. Finally, (iv) we evaluate the method extensively on the FineGym and Diving48 benchmarks for fine-grained action classification and surpass the state-of-the-art using only RGB features. Project page: https://***/-vgg/research/tqn/.

关键词： Training Location awareness computer vision computer architecture pattern recognition Videos

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SPECTRE: Visual Speech-Informed Perceptual 3D Facial Expression Reconstruction from Videos

SPECTRE: Visual Speech-Informed Perceptual 3D Facial Express...

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

作者： Panagiotis P. Filntisis George Retsinas Foivos Paraperas-Papantoniou Athanasios Katsamanis Anastasios Roussos Petros Maragos Institute of Robotics Athena Research Center Maroussi Greece School of Electrical & Computer Engineering National Technical University of Athens Greece Imperial College London UK Institute for Language and Speech Processing Athena R.C. Greece Institute of Computer Science (ICS) Foundation for Research & Technology - Hellas (FORTH) Greece College of Engineering Mathematics and Physical Sciences University of Exeter UK

The recent state of the art on monocular 3D face reconstruction from image data has made some impressive advancements, thanks to the advent of Deep Learning. However, it has mostly focused on input coming from a single RGB image, overlooking the following important factors: a) Nowadays, the vast majority of facial image data of interest do not originate from single images but rather from videos, which contain rich dynamic information. b) Furthermore, these videos typically capture individuals in some form of verbal communication (public talks, teleconferences, audiovisual human-computer interactions, interviews, monologues/dialogues in movies, etc). When existing 3D face reconstruction methods are applied in such videos, the artifacts in the reconstruction of the shape and motion of the mouth area are often severe, since they do not match well with the speech *** overcome the aforementioned limitations, we present the first method for visual speech-informed perceptual reconstruction of 3D mouth expressions. We do this by proposing a "lipreading" loss, which guides the fitting process so that the elicited perception from the 3D reconstructed talking head resembles that of the original video footage. We demonstrate that, interestingly, the lipreading loss is better suited for 3D reconstruction of mouth movements compared to traditional landmark losses, and even direct 3D supervision. Furthermore, the devised method does not rely on any text transcriptions or corresponding audio, rendering it ideal for training in unlabeled datasets. We verify the efficiency of our method through objective evaluations on three large-scale datasets, as well as subjective evaluation with two web-based user studies. Project webpage: https://***/spectre/

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A case for using rotation invariant features in state of the art feature matchers

A case for using rotation invariant features in state of the...

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

作者： Georg Bö kman Fredrik Kahl Chalmers University of Technology

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translations and image rotations. It is experimentally shown that this boost is obtained without reducing performance on ordinary illumination and viewpoint matching sequences.

关键词： computer vision conferences Lighting pattern matching

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Energy-Efficient Uncertainty-Aware Biomass Composition Prediction at the Edge

Energy-Efficient Uncertainty-Aware Biomass Composition Predi...

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

作者： Muhammad Zawish Paul Albert Flavio Esposito Steven Davy Lizy Abraham Walton Institute South East Technological University Ireland Center for Augmented Reasoning Australian Institute of Machine Learning Australia Department of Computer Science Saint Louis University USA Centre for Sustainable Digital Technologies at Technological University Dublin Ireland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Clover fixates nitrogen from the atmosphere to the ground, making grass-clover mixtures highly desirable to reduce external nitrogen fertilization. Herbage containing clover additionally promotes higher food intake, resulting in higher milk production. Herbage probing however remains largely unused as it requires a time-intensive manual laboratory analysis. Without this information, farmers are unable to perform localized clover sowing or take targeted fertilization decisions. Deep learning algorithms have been proposed with the goal to estimate the dry biomass composition from images of the grass directly in the fields. The energy-intensive nature of deep learning however limits deployment to practical edge devices such as smartphones. This paper proposes to fill this gap by applying filter pruning to reduce the energy requirement of existing deep learning solutions. We report that although pruned networks are accurate on controlled, high-quality images of the grass, they struggle to generalize to real-world smartphone images that are blurry or taken from challenging angles. We address this challenge by training filter-pruned models using a variance attenuation loss so they can predict the uncertainty of their predictions. When the uncertainty exceeds a threshold, we re-infer using a more accurate unpruned model. This hybrid approach allows us to reduce energy-consumption while retaining a high accuracy. We evaluate our algorithm on two datasets: the GrassClover and the Irish clover using an NVIDIA Jetson Nano edge device. We find that we reduce energy reduction with respect to state-of-the-art solutions by 50% on average with only 4% accuracy loss.

关键词： Deep learning Accuracy Uncertainty Image edge detection Filtering algorithms Predictive models Prediction algorithms

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MoCap-to-Visual Domain Adaptation for Efficient Human Mesh Estimation from 2D Keypoints

MoCap-to-Visual Domain Adaptation for Efficient Human Mesh E...

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

作者： Bedirhan Uguz Ozhan Suat Batuhan Karagoz Emre Akbas Department of Computer Engineering Middle East Technical University Ankara Turkey METU ROMER Robotics Center Middle East Technical University Ankara Turkey

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents Key2Mesh, a model that takes a set of 2D human pose keypoints as input and estimates the corresponding body mesh. Since this process does not involve any visual (i.e. RGB image) data, the model can be trained on large-scale motion capture (MoCap) datasets, thereby overcoming the scarcity of image datasets with 3D labels. To enable the model’s application on RGB images, we first run an off-the-shelf 2D pose estimator to obtain the 2D keypoints, and then feed these 2D keypoints to Key2Mesh. To improve the performance of our model on RGB images, we apply an adversarial domain adaptation (DA) method to bridge the gap between the MoCap and visual domains. Crucially, our DA method does not require 3D labels for visual data, which enables adaptation to target sets without the need for costly labels. We evaluate Key2Mesh for the task of estimating 3D human meshes from 2D key-points, in the absence of RGB and mesh label pairs. Our results on widely used H3.6M and 3DPW datasets show that Key2Mesh sets the new state-of-the-art by outperforming other models in PA-MPJPE for both datasets, and in MPJPE and PVE for the 3DPW dataset. Thanks to our model’s simple architecture, it operates at least 12× faster than the prior state-of-the-art model, LGD [44]. Additional qualitative samples and code are available on the project website: https://***/.

关键词： Training Adaptation models Visualization Solid modeling Three-dimensional displays Computational modeling Training data

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HNN: Hierarchical Noise-Deinterlace Net Towards Image Denoising

HNN: Hierarchical Noise-Deinterlace Net Towards Image Denois...

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

作者： Amogh Joshi Nikhil Akalwadi Chinmayee Mandi Chaitra Desai Ramesh Ashok Tabib Ujwala Patil Uma Mudenagudi Center of Excellence in Visual Intelligence (CEVI) KLE Technological University Hubballi Karnataka India School of Computer Science and Engineering KLE Technological University Hubballi Karnataka India School of Electronics and Communication Engineering KLE Technological University Hubballi Karnataka India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we propose a hierarchical framework for image denoising and term it Hierarchical Noise-Deinterlace Net (HNN). Image denoising techniques aim to recover clean images from noisy observations by reducing unwanted noise and artifacts to enhance the clarity and introduce spatial coherence. Images captured during challenging scenarios suffer from granular noise, inducing fine-scale variations in the image, which occur due to the limitations of imaging technology or environmental conditions. This granular noise can significantly degrade the quality of the image, making it less useful for applications like object detection, image restoration/enhancement, face detection, and image super-resolution. From literature, we infer learning global-local features significantly contribute in reducing unwanted noise and artifacts within images. Typically, researchers rely on residual learning, Generative Adversarial Networks (GANs), and Attention Mechanisms to learn global-local features. However, these methods face challenges such as vanishing gradients, limited generalization of generators in GANs, lack of global context awareness and computation complexity in attention mechanisms leading to drop in performance. Towards this, we propose a hierarchical framework to process both global and local information across distinct levels of hierarchy. More specifically, we propose a hierarchical encoder-decoder network, with a distinct Global-Local Spatio-Contextual (GLSC) block for learning of fine-grained features and high-frequency details in an image. The proposed framework improves image denoising, as it allows the model to capture and utilize information from different scales, ensuring a comprehensive understanding of the image content. We demonstrate the efficacy of proposed HNN framework, on benchmark datasets in comparison with state-of-the-art methods with 5% (↑ in dB) increase in performance.

关键词： Attention mechanisms Noise Superresolution Spatial coherence Imaging Object detection Benchmark testing

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Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology

Source-Free Domain Adaptation of Weakly-Supervised Object Lo...

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

作者： Alexis Guichemerre Soufiane Belharbi Tsiry Mayet Shakeeb Murtaza Pourya Shamsolmoali Luke McCaffrey Eric Granger Dept. of Systems Engineering LIVIA ETS Montreal Canada INSA LITIS UR 4108 Rouen France School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast UK Dept. of Oncology Goodman Cancer Research Centre McGill University Montreal Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Given the emergence of deep learning, digital pathology has gained popularity for cancer diagnosis based on histology images. Deep weakly supervised object localization (WSOL) models can be trained to classify histology images according to cancer grade and identify regions of interest (ROIs) for interpretation, using inexpensive global image-class annotations. A WSOL model initially trained on some labeled source image data can be adapted using unlabeled target data in cases of significant domain shifts caused by variations in staining, scanners, and cancer type. In this paper, we focus on source-free (unsupervised) domain adaptation (SFDA), a challenging problem where a pre-trained source model is adapted to a new target domain without using any source domain data for privacy and efficiency reasons. SFDA of WSOL models raises several challenges in histology, most notably because they are not intended to adapt for both classification and localization tasks. In this paper, 4 state-of-the-art SFDA methods, each one representative of a main SFDA family, are compared for WSOL in terms of classification and localization accuracy. They are the SFDA-Distribution Estimation, Source HypOthesis Transfer, Cross-Domain Contrastive Learning, and Adaptively Domain Statistics Alignment. Experimental results on the challenging Glas (smaller, breast cancer) and Came-lyon16 (larger, colon cancer) histology datasets indicate that these SFDA methods typically perform poorly for localization after adaptation when optimized for classification. Code: ***/AlexisGuichemerreCode/survey_hist_wsol_sfda

关键词： Location awareness Deep learning Adaptation models Data privacy Histopathology Estimation Contrastive learning

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