To enhance precision and comprehensiveness in identifying targets in electric power construction monitoring video, a novel target recognition algorithm utilizing infrared imaging is explored. This algorithm employs a color processing technique based on a local linear mapping method to effectively recolor monitoring images. The process involves three key steps: color space conversion, color transfer, and pseudo-color encoding. It is designed to accentuate targets in the infrared imaging. For the refined identification of these targets, the algorithm leverages a support vector machine approach, utilizing an optimal hyperplane to accurately predict target types. We demonstrate the efficacy of the algorithm, which achieves high target recognition accuracy in both outdoor and indoor electric power construction monitoring scenarios. It maintains a false recognition rate below 3% across various environments.
With an increasing number of elders living alone, care-giving from a distance becomes a compelling need, particularly for safety. Real-time monitoring and action recognition are essential to raise an alert timely when abnormal behaviors or unusual activities occur. While wearable sensors are widely recognized as a promising solution, highly depending on user's ability and willingness makes them inefficient. In contrast, video streams collected through non-contact optical cameras provide richer information and release the burden on elders. In this paper, leveraging the Independently-Recurrent n…
Wearing a seatbelt appropriately while driving can reduce serious crash-related injuries or deaths by about half. However, current seatbelt reminder system has multiple shortcomings, such as can be easily fooled by a "Seatbelt Warning Stopper", and cannot recognize incorrect usages for example seating in front of a buckled seatbelt or wearing a seatbelt under the arm. General seatbelt usage recognition has many challenges, to name a few, lacking of color information in Infrared (IR) cameras, strong distortion caused by wide Field of View (FoV) fisheye lens, low contrast between belt and its ba…
Politecnico di Milano（米兰理工大学）；STMicroelectronics（意法半导体）
The chips contained in any electronic device are manufactured over circular silicon wafers, which are monitored by inspection machines at different production stages. Inspection machines detect and locate any defect within the wafer and return a Wafer Defect Map (WDM), i.e., a list of the coordinates where defects lie, which can be considered a huge, sparse, and binary image. In normal conditions, wafers exhibit a small number of randomly distributed defects, while defects grouped in specific patterns might indicate known or novel categories of failures in the production line. Needless to say,…
Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a tr…
Falling of elderly people who are staying alone at home leads to health risks. If they are not attended immediately even it may lead to fatal danger to their life. In this paper a novel computer vision-based system for smart monitoring of elderly people using Series Convolutional Neural Network (SCNN) with transfer learning is proposed. When CNN is trained by the frames of the videos directly, it learns from all pixels including the background pixels. Generally, the background in a video does not contribute anything in identifying the action and actually it will mislead the action classificati…
Modern industry requires modern solutions for monitoring the automatic production of goods. Smart monitoring of the functionality of the mechanical parts of technology systems or machines is mandatory for a fully automatic production process. Although Deep Learning has been advancing, allowing for real-time object detection and other tasks, little has been investigated about the effectiveness of specially designed Convolutional Neural Networks for defect detection and industrial object recognition. In the particular study, we employed six publically available industrial-related datasets contai…
Art University of Tehran（德黑兰艺术大学）；Ferdowsi University of Mashhad（马什哈德菲尔多西大学）；Islamic Azad University Taft（塔夫伊斯兰阿扎德大学）；Dibrugarh University（迪布鲁格尔大学）；Deakin University（迪肯大学）
Recent advancements in Artificial intelligence, especially deep learning, has changed many fields irreversibly by introducing state of the art methods for automation. Construction monitoring has not been an exception; as a part of construction monitoring systems, material classification and recognition have drawn the attention of deep learning and machine vision researchers. However, to create production-ready systems, there is still a long path to cover. Real-world problems such as varying illuminations and reaching acceptable accuracies need to be addressed in order to create robust systems.…
Bearded Dragon Activity Recognition Pipeline: An AI-Based Approach to Behavioural Monitoring
Department of Computer Science, Nottingham Trent University（计算机科学系，诺丁汉特伦特大学）；School of Animal, Rural and Environmental Sciences, Nottingham Trent University（动物、农村与环境科学学院，诺丁汉特伦特大学）
Train Pilot is a very tedious and stressful job. Pilots must be vigilant at all times and its easy for them to lose track of time of shift. In countries like USA the pilots are mandated by law to adhere to 8 hour shifts. If they exceed 8 hours of shift the railroads may be penalized for over-tiring their drivers. The problem happens when the 8 hour shift may end in middle of a journey. In such case, the new drivers must be moved to the location locomotive is operating for shift change. Hence accurate monitoring of drivers during their shift and making sure the shifts are scheduled correctly is…
Constructive Cybernetics Research Group（建设性控制论研究组）；Institute of Control Sciences, Russian Academy of Sciences（俄罗斯科学院控制科学研究所）
In this paper, we show an approach to build deep learning algorithms for recognizing signals in distributed fiber optic monitoring and security systems for long perimeters. Synthesizing such detection algorithms poses a non-trivial research and development challenge, because these systems face stringent error (type I and II) requirements and operate in difficult signal-jamming environments, with intensive signal-like jamming and a variety of changing possible signal portraits of possible recognized events. To address these issues, we have developed a twolevel event detection architecture, wher…
Visual search of relevant targets in the environment is a crucial robot skill. We propose a preliminary framework for the execution monitor of a robot task, taking care of the robot attitude to visually searching the environment for targets involved in the task. Visual search is also relevant to recover from a failure. The framework exploits deep reinforcement learning to acquire a "common sense" scene structure and it takes advantage of a deep convolutional network to detect objects and relevant relations holding between them. The framework builds on these methods to introduce a vision-based …
With the recent developments in neural networks, there has been a resurgence in algorithms for the automatic generation of simulation ready electronic circuits from hand-drawn circuits. However, most of the approaches in literature were confined to classify different types of electrical components and only a few of those methods have shown a way to rebuild the circuit schematic from the scanned image, which is extremely important for further automation of netlist generation. This paper proposes a real-time algorithm for the automatic recognition of hand-drawn electrical circuits based on objec…
ElectricSight: 3D Hazard Monitoring for Power Lines Using Low-Cost Sensors
School of Computer Science and Technology, University of Science and Technology of China (USTC)（中国科学技术大学计算机科学与技术学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Recently, there has been a growing interest in monitoring brain activity for individual recognition system. So far these works are mainly focussing on single channel data or fragment data collected by some advanced brain monitoring modalities. In this study we propose new individual recognition schemes based on spatio-temporal resting state Electroencephalography (EEG) data. Besides, instead of using features derived from artificially-designed procedures, modified deep learning architectures which aim to automatically extract an individual's unique features are developed to conduct classificat…
Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoring
McGill University（麦吉尔大学）；Mila - Quebec Artificial Intelligence Institute（魁北克人工智能研究所）；University of Copenhagen（哥本哈根大学）；Agriculture and Agri-food Canada（加拿大农业与农业食品部）；Smithsonian Tropical Research Institute（史密松尼热带研究所）；Biology Center, Czech Academy of Sciences（捷克科学院生物学中心）；Maestria de Entomologia, University of Panama（巴拿马大学昆虫学系）；Massachusetts Institute of Technology（麻省理工学院）；Montréal Insectarium（蒙特利尔昆虫馆）
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Yahoo Labs（雅虎实验室）
Fisher vector has been widely used in many multimedia retrieval and visual recognition applications with good performance. However, the computation complexity prevents its usage in real-time video monitoring. In this work, we proposed and implemented GPU-FV, a fast Fisher vector extraction method with the help of modern GPUs. The challenge of implementing Fisher vector on GPUs lies in the data dependency in feature extraction and expensive memory access in Fisher vector computing. To handle these challenges, we carefully designed GPU-FV in a way that utilizes the computing power of GPU as much…
Dietary studies showed that dietary-related problem such as obesity is associated with other chronic diseases like hypertension, irregular blood sugar levels, and increased risk of heart attacks. The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, which are manageable using interactive mHealth apps. However, traditional dietary monitoring systems using manual food logging suffer from imprecision, underreporting, time consumption, and low adherence. Recent dietary monitoring systems tackle these challenges by automatic assessment of dietary intake through…
Monitoring awkward postures is a proactive prevention for Musculoskeletal Disorders (MSDs)in construction. Machine Learning (ML) models have shown promising results for posture recognition from Wearable Sensors. However, further investigations are needed concerning: i) Incremental Learning (IL), where trained models adapt to learn new postures and control the forgetting of learned postures; ii) MSDs assessment with recognized postures. This study proposed an incremental Convolutional Long Short-Term Memory (CLN) model, investigated effective IL strategies, and evaluated MSDs assessment using r…
Bialystok University of Technology（比亚韦斯托克理工大学）；Mammal Research Institute, Polish Academy of Sciences（波兰科学院哺乳动物研究所）
Camera traps are used worldwide to monitor wildlife. Despite the increasing availability of Deep Learning (DL) models, the effective usage of this technology to support wildlife monitoring is limited. This is mainly due to the complexity of DL technology and high computing requirements. This paper presents the implementation of the light-weight and state-of-the-art YOLOv5 architecture for automated labeling of camera trap images of mammals in the Bialowieza Forest (BF), Poland. The camera trapping data were organized and harmonized using TRAPPER software, an open source application for managin…
Detecting when eating occurs is an essential step toward automatic dietary monitoring, medication adherence assessment, and diet-related health interventions. Wearable technologies play a central role in designing unubtrusive diet monitoring solutions by leveraging machine learning algorithms that work on time-series sensor data to detect eating moments. While much research has been done on developing activity recognition and eating moment detection algorithms, the performance of the detection algorithms drops substantially when the model trained with one user is utilized by a new user. To fac…
Research on Detection of Floating Objects in River and Lake Based on AI Intelligent Image Recognition
The University of Chicago（芝加哥大学）；University of Electronic Science and Technology of China（电子科技大学）；Columbia University（哥伦比亚大学）；Washington University in St. Louis（圣路易斯华盛顿大学）
Faculty of Engineering, Multimedia University（多媒体大学工程学院）；Multimedia University（多媒体大学）
This study is to investigate and compare the facial recognition accuracy performance of Dlib ResNet against a K-Nearest Neighbour (KNN) classifier. Particularly when used against a dataset from an Asian ethnicity as Dlib ResNet was reported to have an accuracy deficiency when it comes to Asian faces. The comparisons are both implemented on the facial vectors extracted using the Histogram of Oriented Gradients (HOG) method and use the same dataset for a fair comparison. Authentication of a user by facial recognition in an electric vehicle (EV) charging station demonstrates a practical use case …
Prince Sultan University（苏丹王子大学）；Polytechnic Institute of Porto（波尔图理工学院）；Gaitech Robotics（盖泰克机器人公司）；National Engineering School of Carthage（迦太基国家工程学院）
In the Muslim community, the prayer (i.e. Salat) is the second pillar of Islam, and it is the most essential and fundamental worshiping activity that believers have to perform five times a day. From a gestures' perspective, there are predefined human postures that must be performed in a precise manner. However, for several people, these postures are not correctly performed, due to being new to Salat or even having learned prayers in an incorrect manner. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an artificial intelligence assi…
Department of Electrical and Computer Engineering, University of Arizona（电子与计算机工程系，亚利桑那大学）；Department of Computing and Data Science, University of California, Berkeley（计算与数据科学系，加州大学伯克利分校）；Quandary Peak Research（Quandary Peak研究公司）；Department of Systems and Industrial Engineering, University of Arizona（系统与工业工程系，亚利桑那大学）；College of Information Sciences, University of Arizona（信息科学学院，亚利桑那大学）
