This paper has provided a novel design idea and some implementation methods to make a real time detection of multi-areas with multiple detecting areas that are generated by the real time drawing on the screen display of the video. The drawing on the video will remain the output as polylines, and the colors of the outlines will change when the stage of drawing or detecting is changed. The shape of the drawn area is free to be customized and real-time effective. The configuration of the drawn areas can be renewed and the detecting areas are working individually. The detection result should be shown with a GUI designed by Tkinter. The object recognition model was developed on YOLOv5 but can be changed to others, which means the core design and implementation idea of this paper is model-independent. With PIL and OpenCV and Tkinter, the drawing effect is real time and efficient. The design and code of this research is basic and can be extended to be implemented in numerous monitoring and detecting situations.
Artrendex LLC（Artrendex有限责任公司）；Rutgers University（罗格斯大学）；Atelier for Restoration & Research of Paintings (ARRS)（绘画修复与研究工作室）
This paper proposes a computational approach for analysis of strokes in line drawings by artists. We aim at developing an AI methodology that facilitates attribution of drawings of unknown authors in a way that is not easy to be deceived by forged art. The methodology used is based on quantifying the characteristics of individual strokes in drawings. We propose a novel algorithm for segmenting individual strokes. We designed and compared different hand-crafted and learned features for the task of quantifying stroke characteristics. We also propose and compare different classification methods a…
In the past, manually re-drawing an image in a certain artistic style required a professional artist and a long time. Doing this for a video sequence single-handed was beyond imagination. Nowadays computers provide new possibilities. We present an approach that transfers the style from one image (for example, a painting) to a whole video sequence. We make use of recent advances in style transfer in still images and propose new initializations and loss functions applicable to videos. This allows us to generate consistent and stable stylized video sequences, even in cases with large motion and s…
Weakly Supervised Video Individual CountingWeakly Supervised Video Individual Counting
University of Chinese Academy of Science（中国科学院大学）；Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；University of California, Merced（加州大学默塞德分校）；Peng Cheng Laboratory（鹏城实验室）
A non-invasive video-based method for individual identification of wildlife using gait dynamics
Department of Computer Science, University of Oxford（牛津大学计算机科学系）；Wildlife Conservation Research Unit, Department of Biology, University of Oxford（牛津大学生物系野生动物保护研究组）
University of Melbourne（墨尔本大学）；Monash University（莫纳什大学）；Tsinghua University（清华大学）
Video summarization aims to automatically generate a summary (storyboard or video skim) of a video, which can facilitate large-scale video retrieval and browsing. Most of the existing methods perform video summarization on individual videos, which neglects the correlations among similar videos. Such correlations, however, are also informative for video understanding and video summarization. To address this limitation, we propose Video Joint Modelling based on Hierarchical Transformer (VJMHT) for co-summarization, which takes into consideration the semantic dependencies across videos. Specifica…
University of Campania “Luigi Vanvitelli”（坎帕尼亚大学“路易吉·万维泰利”）；TecnoCampus Mataro-Maresme Escola Superior Politecnica Mataro（马塔罗-马雷斯梅科技园区马塔罗高等理工学院）；University of Glasgow（格拉斯哥大学）
The events of the past 2 years related to the pandemic have shown that it is increasingly important to find new tools to help mental health experts in diagnosing mood disorders. Leaving aside the longcovid cognitive (e.g., difficulty in concentration) and bodily (e.g., loss of smell) effects, the short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients. To this aim, patients with clinical dep…
In this paper we publish the largest identity-annotated Holstein-Friesian cattle dataset Cows2021 and a first self-supervision framework for video identification of individual animals. The dataset contains 10,402 RGB images with labels for localisation and identity as well as 301 videos from the same herd. The data shows top-down in-barn imagery, which captures the breed's individually distinctive black and white coat pattern. Motivated by the labelling burden involved in constructing visual cattle identification systems, we propose exploiting the temporal coat pattern appearance across videos…
Manually re-drawing an image in a certain artistic style takes a professional artist a long time. Doing this for a video sequence single-handedly is beyond imagination. We present two computational approaches that transfer the style from one image (for example, a painting) to a whole video sequence. In our first approach, we adapt to videos the original image style transfer technique by Gatys et al. based on energy minimization. We introduce new ways of initialization and new loss functions to generate consistent and stable stylized video sequences even in cases with large motion and strong oc…
Uber Advanced Technologies Group（优步先进技术集团）；University of Toronto（多伦多大学）；MIT（麻省理工学院）
Annotating videos with object segmentation masks typically involves a two stage procedure of drawing polygons per object instance for all the frames and then linking them through time. While simple, this is a very tedious, time consuming and expensive process, making the creation of accurate annotations at scale only possible for well-funded labs. What if we were able to segment an object in the full video with only a single click? This will enable video segmentation at scale with a very low budget opening the door to many applications. Towards this goal, in this paper we propose a bottom up a…
Universidade da Coruña（拉科鲁尼亚大学）；CITIC（CITIC（计算机技术与系统研究中心））
Tracking people in a video sequence is a challenging task that has been approached from many perspectives. This task becomes even more complicated when the person to track is a player in a broadcasted sport event, the reasons being the existence of difficulties such as frequent camera movements or switches, total and partial occlusions between players, and blurry frames due to the codification algorithm of the video. This paper introduces a player tracking solution which is both fast and accurate. This allows to track a player precisely in real-time. The approach combines several models that a…
Northwestern Polytechnical University（西北工业大学）；The University of Sydney（悉尼大学）；SenseTime（商汤科技）
Pedestrian counting is a fundamental tool for understanding pedestrian patterns and crowd flow analysis. Existing works (e.g., image-level pedestrian counting, crossline crowd counting et al.) either only focus on the image-level counting or are constrained to the manual annotation of lines. In this work, we propose to conduct the pedestrian counting from a new perspective - Video Individual Counting (VIC), which counts the total number of individual pedestrians in the given video (a person is only counted once). Instead of relying on the Multiple Object Tracking (MOT) techniques, we propose t…
Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data
University of Applied Sciences Darmstadt（达姆施塔特应用科学大学）；Max Planck Institute for Evolutionary Anthropology（马克斯·普朗克进化人类学研究所）；German Centre for Integrative Biodiversity Research（德国综合生物多样性研究中心）；University of Victoria（维多利亚大学）；University of Osnabrück（奥斯纳布吕克大学）；Senckenberg Museum of Natural History Goerlitz（戈利茨森肯伯格自然历史博物馆）；Technische Universität Dresden（德累斯顿工业大学）；Harvard University（哈佛大学）；University of St. Andrews（圣安德鲁斯大学）；University of Lyon 1（里昂第一大学）；Centre Suisse de Recherches Scientifiques（瑞士科学研究中心）；University of KwaZulu-Natal（夸祖鲁-纳塔尔大学）
Video analytics is the practice of combining digital video data with machine learning models to infer various characteristics from that video. This capability has been used for years to detect objects, movement, and the number of customers in physical retail stores, but more complex machine learning models combined with more powerful computing power has unlocked new levels of possibility. Researchers claim it is now possible to infer a whole host of characteristics about an individual using video analytics, such as specific age, ethnicity, health status and emotional state. Moreover, an indivi…
The University of Adelaide（阿德莱德大学）；Australian Centre for Robotic Vision（澳大利亚机器人视觉中心）；Australian National University（澳大利亚国立大学）
The state-of-the art solutions for human activity understanding from a video stream formulate the task as a spatio-temporal problem which requires joint localization of all individuals in the scene and classification of their actions or group activity over time. Who is interacting with whom, e.g. not everyone in a queue is interacting with each other, is often not predicted. There are scenarios where people are best to be split into sub-groups, which we call social groups, and each social group may be engaged in a different social activity. In this paper, we solve the problem of simultaneously…
Pose2Gait: Extracting Gait Features from Monocular Video of Individuals with Dementia
University of Toronto（多伦多大学）；KITE Research Institute, Toronto Rehabilitation Institute, University Health Network（多伦多康复研究所KITE研究所，大学健康网络）；The Hospital for Sick Children（病童医院）；Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network（多伦多西部医院莫顿与格洛丽亚·舒尔曼运动障碍诊所，大学健康网络）；Krembil Brain Institute, University Health Network（大学健康网络克雷姆比尔脑研究所）；Center for Advancing Neurotechnological Innovation to Application（神经技术创新应用推进中心）；Centre for Mental Health, University Health Network（大学健康网络心理健康中心）
Escuela de Ingenieria y Ciencias, Tecnologico de Monterrey（蒙特雷理工学院工程与科学学院）；CRAN (UMR 7039), Université de Lorraine（洛林大学CRAN（UMR 7039））；CNRS（法国国家科学研究中心）
In this contribution we use an ensemble deep-learning method for combining the prediction of two individual one-stage detectors (i.e., YOLOv4 and Yolact) with the aim to detect artefacts in endoscopic images. This ensemble strategy enabled us to improve the robustness of the individual models without harming their real-time computation capabilities. We demonstrated the effectiveness of our approach by training and testing the two individual models and various ensemble configurations on the "Endoscopic Artifact Detection Challenge" dataset. Extensive experiments show the superiority, in terms o…
We present an integrated framework for simultaneous tracking, group detection and multi-level activity recognition in crowd videos. Instead of solving these problems independently and sequentially, we solve them together in a unified framework to utilize the strong correlation that exists among individual motion, groups, and activities. We explore the hierarchical structure hidden in the video that connects individuals over time to produce tracks, connects individuals to form groups and also connects groups together to form a crowd. We show that estimation of this hidden structure corresponds …
Champalimaud Research, Champalimaud Center for the Unknown（尚帕利莫德研究所，尚帕利莫德未知中心）
Our understanding of collective animal behavior is limited by our ability to track each of the individuals. We describe an algorithm and software, idtracker.ai, that extracts from video all trajectories with correct identities at a high accuracy for collectives of up to 100 individuals. It uses two deep networks, one detecting when animals touch or cross and another one for animal identification, trained adaptively to conditions and difficulty of the video.
Faculty of Computer Engineering Amirkabi University of Technology Tehran, Iran
Video Generation is a relatively new and yet popular subject in machine learning due to its vast variety of potential applications and its numerous challenges. Current methods in Video Generation provide the user with little or no control over the exact specification of how the objects in the generate video are to be moved and located at each frame, that is, the user can't explicitly control how each object in the video should move. In this paper we propose a novel method that allows the user to move any number of objects of a single initial frame just by drawing bounding boxes over those obje…
University of Lincoln（林肯大学）；University of Leeds（利兹大学）
Individual pig detection and tracking is an important requirement in many video-based pig monitoring applications. However, it still remains a challenging task in complex scenes, due to problems of light fluctuation, similar appearances of pigs, shape deformations and occlusions. To tackle these problems, we propose a robust real time multiple pig detection and tracking method which does not require manual marking or physical identification of the pigs, and works under both daylight and infrared light conditions. Our method couples a CNN-based detector and a correlation filter-based tracker vi…
Crowded Video Individual Counting Informed by Social Grouping and Spatial-Temporal Displacement Priors
State Key Laboratory of Multispectral Information Intelligent Processing Technology（多谱信息智能处理技术国家重点实验室；人工智能与自动化学院，华中科技大学）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（湖北省智能机器人重点实验室；计算机科学与工程人工智能学院，武汉理工大学）；Hubei Key Laboratory of Intelligent Robot（软件工程学院，华中科技大学）；School of Computer Science & Engineering Artificial Intelligence, Wuhan Institute of Technology；School of Software Engineering, Huazhong University of Science and Technology
In this paper we present an approach for classifying the activity performed by a group of people in a video sequence. This problem of group activity recognition can be addressed by examining individual person actions and their relations. Temporal dynamics exist both at the level of individual person actions as well as at the level of group activity. Given a video sequence as input, methods can be developed to capture these dynamics at both person-level and group-level detail. We build a deep model to capture these dynamics based on LSTM (long short-term memory) models. In order to model both p…
Recognizing group activities is challenging due to the difficulties in isolating individual entities, finding the respective roles played by the individuals and representing the complex interactions among the participants. Individual actions and group activities in videos can be represented in a common framework as they share the following common feature: both are composed of a set of low-level features describing motions, e.g., optical flow for each pixel or a trajectory for each feature point, according to a set of composition constraints in both temporal and spatial dimensions. In this pape…
We present a method for retiming people in an ordinary, natural video -- manipulating and editing the time in which different motions of individuals in the video occur. We can temporally align different motions, change the speed of certain actions (speeding up/slowing down, or entirely "freezing" people), or "erase" selected people from the video altogether. We achieve these effects computationally via a dedicated learning-based layered video representation, where each frame in the video is decomposed into separate RGBA layers, representing the appearance of different people in the video. A ke…
Autism often remains undiagnosed in adolescents and adults. Prior research has indicated that an autistic individual often shows atypical fixation and gaze patterns. In this short paper, we demonstrate that by monitoring a user's gaze as they watch commonplace (i.e., not specialized, structured or coded) video, we can identify individuals with autism spectrum disorder. We recruited 35 autistic and 25 non-autistic individuals, and captured their gaze using an off-the-shelf eye tracker connected to a laptop. Within 15 seconds, our approach was 92.5% accurate at identifying individuals with an au…
Sun Yat-sen University（中山大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Hong Kong University of Science and Technology（香港科学大学）；City University of Hong Kong（香港城市大学）
A Personalized Video-Based Hand Taxonomy: Application for Individuals with Spinal Cord Injury
Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）；Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto（多伦多大学爱德华·S·罗杰斯 Sr. 电气与计算机工程系）；Vector Institute（向量研究所）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）
Charotar University of Science and Technology（查罗塔科技大学）
Violence rates however have been brought down about 57% during the span of the past 4 decades yet it doesn't change the way that the demonstration of violence actually happens, unseen by the law. Violence can be mass controlled sometimes by higher authorities, however, to hold everything in line one must "Microgovern" over each movement occurring in every road of each square. To address the butterfly effects impact in our setting, I made a unique model and a theorized system to handle the issue utilizing deep learning. The model takes the input of the CCTV video feeds and after drawing inferen…
In this paper we present a number of methods (manual, semi-automatic and automatic) for tracking individual targets in high density crowd scenes where thousand of people are gathered. The necessary data about the motion of individuals and a lot of other physical information can be extracted from consecutive image sequences in different ways, including optical flow and block motion estimation. One of the famous methods for tracking moving objects is the block matching method. This way to estimate subject motion requires the specification of a comparison window which determines the scale of the …
Binghamton University, State University of New York（纽约州立大学宾汉姆顿分校）；Waseda University（早稻田大学）；Drexel University（德雷塞尔大学）；SUNY College of Environmental Science and Forestry（纽约州立大学环境科学与林业学院）
We propose a novel computational method to extract information about interactions among individuals with different behavioral states in a biological collective from ordinary video recordings. Assuming that individuals are acting as finite state machines, our method first detects discrete behavioral states of those individuals and then constructs a model of their state transitions, taking into account the positions and states of other individuals in the vicinity. We have tested the proposed method through applications to two real-world biological collectives: termites in an experimental setting…
Mining Multi-Modality Spatio-Temporal Cues for Video Important Person Identification
School of Computer Science and Technology, Wuhan University of Science and Technology（武汉科技大学计算机科学与技术学院）；Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology（湖北省智能信息处理与实时工业系统重点实验室）；School of Computer Science, National Engineering Research Center for Multimedia Software, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University（计算机科学学院，国家多媒体软件工程技术研究中心，湖北省多媒体与网络通信工程重点实验室，武汉大学）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
Recent advances in artificial intelligence make it progressively hard to distinguish between genuine and counterfeit media, especially images and videos. One recent development is the rise of deepfake videos, based on manipulating videos using advanced machine learning techniques. This involves replacing the face of an individual from a source video with the face of a second person, in the destination video. This idea is becoming progressively refined as deepfakes are getting progressively seamless and simpler to compute. Combined with the outreach and speed of social media, deepfakes could ea…
