This paper considers the task of detecting the ball from a single viewpoint in the challenging but common case where the ball interacts frequently with players while being poorly contrasted with respect to the background. We propose a novel approach by formulating the problem as a segmentation task solved by an efficient CNN architecture. To take advantage of the ball dynamics, the network is fed with a pair of consecutive images. Our inference model can run in real time without the delay induced by a temporal analysis. We also show that test-time data augmentation allows for a significant increase the detection accuracy. As an additional contribution, we publicly release the dataset on which this work is based.
Toshiba Europe Ltd（东芝欧洲有限公司）；University of Cambridge（剑桥大学）
Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagation and attenuation, perspective viewing geometry and specular light reflection are considered. Many of works tackling Photometric Stereo (PS) problems often relax most of the aforementioned assumptions. Especially they ignore specular reflection and global illumination effects. In this work, we propose a CNN-based approach capable of handling these realistic assumptions by leveraging recent improvements of deep neur…
We develop a method for the automated detection and segmentation of speech balloons in comic books, including their carrier and tails. Our method is based on a deep convolutional neural network that was trained on annotated pages of the Graphic Narrative Corpus. More precisely, we are using a fully convolutional network approach inspired by the U-Net architecture, combined with a VGG-16 based encoder. The trained model delivers state-of-the-art performance with an F1-score of over 0.94. Qualitative results suggest that wiggly tails, curved corners, and even illusory contours do not pose a majo…
University of Kansas（堪萨斯大学）；Ainstein Inc.（Ainstein公司）；Ryerson University（瑞尔森大学）
This paper focuses on the problem of online golf ball detection and tracking from image sequences. An efficient real-time approach is proposed by exploiting convolutional neural networks (CNN) based object detection and a Kalman filter based prediction. Five classical deep learning-based object detection networks are implemented and evaluated for ball detection, including YOLO v3 and its tiny version, YOLO v4, Faster R-CNN, SSD, and RefineDet. The detection is performed on small image patches instead of the entire image to increase the performance of small ball detection. At the tracking stage…
Delamination assessment of the bridge deck plays a vital role for bridge health monitoring. Thermography as one of the nondestructive technologies for delamination detection has the advantage of efficient data acquisition. But there are challenges on the interpretation of data for accurate delamination shape profiling. Due to the environmental variation and the irregular presence of delamination size and depth, conventional processing methods based on temperature contrast fall short in accurate segmentation of delamination. Inspired by the recent development of deep learning architecture for i…
Cricket is undoubtedly one of the most popular games in this modern era. As human beings are prone to error, there remains a constant need for automated analysis and decision making of different events in this game. Simultaneously, with advent and advances in Artificial Intelligence and Computer Vision, application of these two in different domains has become an emerging trend. Applying several computer vision techniques in analyzing different Cricket events and automatically coming into decisions has become popular in recent days. In this paper, we have deployed a CNN based classification met…
Soccer ball detection is identified as one of the critical challenges in the RoboCup competition. It requires an efficient vision system capable of handling the task of detection with high precision and recall and providing robust and low inference time. In this work, we present a novel convolutional neural network (CNN) approach to detect the soccer ball in an image sequence. In contrast to the existing methods where only the current frame or an image is used for the detection, we make use of the history of frames. Using history allows to efficiently track the ball in situations where the bal…
Event-based Gaze Control System for Accurate Real-time Spin Estimation in Professional Ball Games
Sony AI, Tokyo, Japan（索尼AI，东京，日本）；Sony AI, Zürich, Switzerland（索尼AI，苏黎世，瑞士）；Sony Advanced Visual Sensing, Sony Europe Ltd., Zurich, Switzerland（索尼高级视觉感知，索尼欧洲有限公司，苏黎世，瑞士）
Granular Ball Guided Masking: Structure-aware Data Augmentation
Chongqing Key Laboratory of Computational Intelligence（重庆计算智能重点实验室）；Key Laboratory of Cyberspace Big Data Intelligent Security（网络大数据智能安全重点实验室）；Sichuan-Chongqing Co-construction Key Laboratory of Digital Economy Intelligence（四川-重庆数字经济智能联合重点实验室）；Key Laboratory of Big Data Intelligent Computing（大数据智能计算重点实验室）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）
Mobile autonomous robots include numerous sensors for environment perception. Cameras are an essential tool for robot's localization, navigation, and obstacle avoidance. To process a large flow of data from the sensors, it is necessary to optimize algorithms, or to utilize substantial computational power. In our work, we propose an algorithm for optimizing a neural network for object detection using preliminary binary frame classification. An autonomous outdoor mobile robot with 6 rolling-shutter cameras on the perimeter providing a 360-degree field of view was used as the experimental setup. …
In this work, the novel task of detecting and classifying table tennis strokes solely using the ball trajectory has been explored. A single camera setup positioned in the umpire's view has been employed to procure a dataset consisting of six stroke classes executed by four professional table tennis players. Ball tracking using YOLOv4, a traditional object detection model, and TrackNetv2, a temporal heatmap based model, have been implemented on our dataset and their performances have been benchmarked. A mathematical approach developed to extract temporal boundaries of strokes using the ball tra…
University of Canterbury（坎特伯雷大学）；HIT Lab NZ（新西兰HIT实验室）
With the emergence of onboard vision processing for areas such as the internet of things (IoT), edge computing and autonomous robots, there is increasing demand for computationally efficient convolutional neural network (CNN) models to perform real-time object detection on resource constraints hardware devices. Tiny-YOLO is generally considered as one of the faster object detectors for low-end devices and is the basis for our work. Our experiments on this network have shown that Tiny-YOLO can achieve 0.14 frames per second(FPS) on the Raspberry Pi 3 B, which is too slow for soccer playing auto…
The University of Winnipeg（温尼伯大学）；Qatar University（卡塔尔大学）；Hamad Medical Corporation（哈马德医疗公司）
Cardiac imaging known as echocardiography is a non-invasive tool utilized to produce data including images and videos, which cardiologists use to diagnose cardiac abnormalities in general and myocardial infarction (MI) in particular. Echocardiography machines can deliver abundant amounts of data that need to be quickly analyzed by cardiologists to help them make a diagnosis and treat cardiac conditions. However, the acquired data quality varies depending on the acquisition conditions and the patient's responsiveness to the setup instructions. These constraints are challenging to doctors especi…
In table tennis, the rotation (spin) of the ball plays a crucial role. A table tennis match will feature a variety of strokes. Each generates different amounts and types of spin. To develop a robot that can compete with a human player, the robot needs to detect spin, so it can plan an appropriate return stroke. In this paper we compare three methods to estimate spin. The first two approaches use a high-speed camera that captures the ball in flight at a frame rate of 380 Hz. This camera allows the movement of the circular brand logo printed on the ball to be seen. The first approach uses backgr…
Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；Department of Industrial Engineering and Operations Research（工业工程与运筹学系）；The AUTOLAB at UC Berkeley（加州大学伯克利分校AUTOLAB）
The ability to segment unknown objects in depth images has potential to enhance robot skills in grasping and object tracking. Recent computer vision research has demonstrated that Mask R-CNN can be trained to segment specific categories of objects in RGB images when massive hand-labeled datasets are available. As generating these datasets is time consuming, we instead train with synthetic depth images. Many robots now use depth sensors, and recent results suggest training on synthetic depth data can transfer successfully to the real world. We present a method for automated dataset generation a…
King's College London（伦敦国王学院）；Guy's and St Thomas' Hospital NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托医院）；Imperial College London（帝国理工学院）
Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare tw…
Max-Planck Institute for Informatics（马克斯-普朗克信息研究所）；Stanford University（斯坦福大学）；CVLAB of EPFL（洛桑联邦理工学院计算机视觉实验室）
We propose the first real-time approach for the egocentric estimation of 3D human body pose in a wide range of unconstrained everyday activities. This setting has a unique set of challenges, such as mobility of the hardware setup, and robustness to long capture sessions with fast recovery from tracking failures. We tackle these challenges based on a novel lightweight setup that converts a standard baseball cap to a device for high-quality pose estimation based on a single cap-mounted fisheye camera. From the captured egocentric live stream, our CNN based 3D pose estimation approach runs at 60H…
Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with CNN-ViT Collaborative Learning
College of Information Science and Technology, Jinan University（暨南大学信息科学技术学院）；Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University（暨南大学广东省中医信息技术重点实验室）；Auckland Bioengineering Institute, University of Auckland（奥克兰大学生物工程研究所）；Universitat de Barcelona（巴塞罗那大学）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚研究与高级研究学院（ICREA））
Istanbul Technical University（伊斯坦布尔理工大学）；Sodec Technologies
Face anti-spoofing is essential to prevent false facial verification by using a photo, video, mask, or a different substitute for an authorized person's face. Most of the state-of-the-art presentation attack detection (PAD) systems suffer from overfitting, where they achieve near-perfect scores on a single dataset but fail on a different dataset with more realistic data. This problem drives researchers to develop models that perform well under real-world conditions. This is an especially challenging problem for frame-based presentation attack detection systems that use convolutional neural net…
Universidade Federal do ABC（ABC联邦大学）；Universidade Federal Fluminense（弗鲁米嫩塞联邦大学）
The representation of consistent mixed reality (XR) environments requires adequate real and virtual illumination composition in real-time. Estimating the lighting of a real scenario is still a challenge. Due to the ill-posed nature of the problem, classical inverse-rendering techniques tackle the problem for simple lighting setups. However, those assumptions do not satisfy the current state-of-art in computer graphics and XR applications. While many recent works solve the problem using machine learning techniques to estimate the environment light and scene's materials, most of them are limited…
