Download or read book A Unified Framework for Human Activity Detection and Recognition for Video Surveillance Using Dezert Smarandache Theory written by Srilatha V. and published by Infinite Study. This book was released on with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt: Trustworthy contextual data of human action recognition of remotely monitored person who requires medical care should be generated to avoid hazardous situation and also to provide ubiquitous services in home-based care. It is difficult for numerous reasons. At first level, the data obtained from heterogeneous source have different level of uncertainty. Second level generated information can be corrupted due to simultaneous operations. In this paper human action recognition can be done based on two different modality consisting of fully featured camera and wearable sensor.

Download or read book Advances in Human Activity Detection and Recognition HADR Systems written by Santosh Kumar Tripathy and published by Springer Nature. This book was released on with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Intelligent Video Surveillance written by Yunqian Ma and published by CRC Press. This book was released on 2009-12-16 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the streets of London to subway stations in New York City, hundreds of thousands of surveillance cameras ubiquitously collect hundreds of thousands of videos, often running 24/7. How can such vast volumes of video data be stored, analyzed, indexed, and searched? How can advanced video analysis and systems autonomously recognize people and

Download or read book Recognition of Humans and Their Activities Using Video written by Rama Chellappa and published by Morgan & Claypool Publishers. This book was released on 2006-01-01 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recognition of humans and their activities from video sequences is currently a very active area of research because of its applications in video surveillance, design of realistic entertainment systems, multimedia communications, and medical diagnosis. In this lecture, we discuss the use of face and gait signatures for human identification and recognition of human activities from video sequences. We survey existing work and describe some of the more well-known methods in these areas. We also describe our own research and outline future possibilities. In the area of face recognition, we start with the traditional methods for image-based analysis and then describe some of the more recent developments related to the use of video sequences, 3D models, and techniques for representing variations of illumination. We note that the main challenge facing researchers in this area is the development of recognition strategies that are robust to changes due to pose, illumination, disguise, and aging. Gait recognition is a more recent area of research in video understanding, although it has been studied for a long time in psychophysics and kinesiology. The goal for video scientists working in this area is to automatically extract the parameters for representation of human gait. We describe some of the techniques that have been developed for this purpose, most of which are appearance based. We also highlight the challenges involved in dealing with changes in viewpoint and propose methods based on image synthesis, visual hull, and 3D models. In the domain of human activity recognition, we present an extensive survey of various methods that have been developed in different disciplines like artificial intelligence, image processing, pattern recognition, and computer vision. We then outline our method for modeling complex activities using 2D and 3D deformable shape theory. The wide application of automatic human identification and activity recognition methods will require the fusion of different modalities like face and gait, dealing with the problems of pose and illumination variations, and accurate computation of 3D models. The last chapter of this lecture deals with these areas of future research.

Download or read book Intelligent Video Surveillance Systems written by Maheshkumar H Kolekar and published by CRC Press. This book was released on 2018-06-27 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will provide an overview of techniques for visual monitoring including video surveillance and human activity understanding. It will present the basic techniques of processing video from static cameras, starting with object detection and tracking. The author will introduce further video analytic modules including face detection, trajectory analysis and object classification. Examining system design and specific problems in visual surveillance, such as the use of multiple cameras and moving cameras, the author will elaborate on privacy issues focusing on approaches where automatic processing can help protect privacy.

Download or read book Intelligent Video Surveillance written by António J. R. Neves and published by . This book was released on 2019-03-13 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of Intelligent video surveillance systems is to efficiently extract useful information from a considerable number of videos collected by surveillance cameras by automatically detecting, tracking and recognizing objects of interest, and understanding and analyzing their activities. Video surveillance has a huge amount of applications, from public to private places. These applications require monitoring indoor and outdoor scenes. Nowadays, there are a considerable number of digital surveillance cameras collecting a huge amount of data on a daily basis. Researchers are urged to develop intelligent systems to efficiently extract and visualize useful information from this big data source. The exponential effort on the development of new algorithms and systems for video surveillance is confirmed by the amount of effort invested in projects and companies, the creation on new startups worldwide and, not less important, in the quantity and quality of the manuscripts published in a considerable number of journals and conferences worldwide. This book is an outcome of research done by several researchers who have highly contributed to the field of Video Surveillance. The main goal is to present recent advances in this important topic for the Image Processing community.

Download or read book Human Activity Recognition and Prediction written by Yun Fu and published by Springer. This book was released on 2015-12-23 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques.

Download or read book Contactless Human Activity Analysis written by Md Atiqur Rahman Ahad and published by Springer Nature. This book was released on 2021-03-23 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a truly comprehensive, timely, and very much needed treatise on the conceptualization of analysis, and design of contactless & multimodal sensor-based human activities, behavior understanding & intervention. From an interaction design perspective, the book provides views and methods that allow for more safe, trustworthy, efficient, and more natural interaction with technology that will be embedded in our daily living environments. The chapters in this book cover sufficient grounds and depth in related challenges and advances in sensing, signal processing, computer vision, and mathematical modeling. It covers multi-domain applications, including surveillance and elderly care that will be an asset to entry-level and practicing engineers and scientists.(See inside for the reviews from top experts)

Download or read book Protecting Privacy in Video Surveillance written by Andrew Senior and published by Springer Science & Business Media. This book was released on 2009-07-06 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Protecting Privacy in Video Surveillance offers the state of the art from leading researchers and experts in the field. This broad ranging volume discusses the topic from various technical points of view and also examines surveillance from a societal perspective. A comprehensive introduction carefully guides the reader through the collection of cutting-edge research and current thinking. The technical elements of the field feature topics from MERL blind vision, stealth vision and privacy by de-identifying face images, to using mobile communications to assert privacy from video surveillance, and using wearable computing devices for data collection in surveillance environments. Surveillance and society is approached with discussions of security versus privacy, the rise of surveillance, and focusing on social control. This rich array of the current research in the field will be an invaluable reference for researchers, as well as graduate students.

Download or read book Video Surveillance Techniques and Technologies written by Zeljkovic, Vesna and published by IGI Global. This book was released on 2013-12-31 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents empirical research and acquired experience on the original solutions and mathematical algorithms for motion detection and object identification problems, emphasizing a wide variety of applications of security systems"--Provided by publisher.

Download or read book Human Activity Recognition Using a Wearable Camera written by Girmaw Abebe Tadesse and published by . This book was released on 2020 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first person videos. The proposed features encode discriminant characteristics form magnitude, direction and dynamics of motion estimated using optical flow. M:>reover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features.Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variaitons of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classi fiers, and the temporal modelling is shown to improve recognition performance.We also investigate the use of deep networks to simplify the feature engineering from first-person videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiplemotion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activiites. Furthermore, we apply cross-domain knowledge transfer between inertial based and vision-based approaches for egocentric activity recognition. We propose sparsity weightedcombination of information from different motion modalities and/or streams . Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity.

Download or read book Intelligent Video Surveillance Systems written by Jean-Yves Dufour and published by John Wiley & Sons. This book was released on 2012-12-14 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Belonging to the wider academic field of computer vision, video analytics has aroused a phenomenal surge of interest since the current millennium. Video analytics is intended to solve the problem of the incapability of exploiting video streams in real time for the purpose of detection or anticipation. It involves analyzing the videos using algorithms that detect and track objects of interest over time and that indicate the presence of events or suspect behavior involving these objects. The aims of this book are to highlight the operational attempts of video analytics, to identify possible driving forces behind potential evolutions in years to come, and above all to present the state of the art and the technological hurdles which have yet to be overcome. The need for video surveillance is introduced through two major applications (the security of rail transportation systems and a posteriori investigation). The characteristics of the videos considered are presented through the cameras which enable capture and the compression methods which allow us to transport and store them. Technical topics are then discussed – the analysis of objects of interest (detection, tracking and recognition), “high-level” video analysis, which aims to give a semantic interpretation of the observed scene (events, behaviors, types of content). The book concludes with the problem of performance evaluation.

Download or read book Recognition Of Humans And Their Activities Using Video written by Rama Chellappa and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Study on Context Driven Human Activity Recognition Framework written by Shatakshi Chakraborty and published by . This book was released on 2015 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent world, human activity recognition has drawn much attention in the field of human computer interaction. There is a growing demand of activity recognition in different areas of everyday living, such as health-care systems like patient health monitoring, home-based rehabilitation, entertainment, and many more. In this research, we are aiming to use activity recognition theory in health-care system to monitor patient behavior during the waiting time at clinical visits. In today's health-care system, patients wait about 22 minutes on average in doctor's offices, and more than four hours in emergency departments. As wait time increases, patient satisfaction drops. With a growing consumer-mindedness of instant gratification or satisfaction, health care providers or hospitals are looking ways to improve productivity, like shortening each patient's path through the health care system, perhaps, adopting measures such as clinics using kiosks, and not reception desks, speedier check-in for returning patients, and taking measures to funnel visitors to the appropriate part of the clinic or hospital when appointments have been arranged earlier, while providing more attentive face-to-face care to those who are first timers to the system and in need. The purpose of this study is to investigate a computer-based means to obtain useful data on typical human behaviors during visits to clinics. A framework to implement the technology to study human behavior has been proposed by Tao Ma[15] recently. In his four-layer hierarchical framework, computer vision is used to study and understand human behavior through body movements. We explore a second framework developed by Saguna et al. [25] which uses probability theory and statistical learning methods to discover complex activity signatures. Additional modalities of information, such as speech, facial expressions, time-based contextual information can also be incorporated to interpret various human behaviors and elicit the cognitive processes used in analyzing the workflow of normal activities. Appertaining to the vast area of human behavior study, a particularly interesting setting to study body movements in analyzing human behavior is in a common venue of our daily life, in particular, typical visit to a doctor's clinic or a hospital. No study has been conducted thus far, to our best knowledge, to understand patient's satisfaction during a clinical visit based on real-time body movements and gestures of the patients in the waiting lounge. In this research work, we further explore the existing framework to represent the small cosmos of waiting rooms in clinic, and to apply mathematical models to derive individual complex behavior, often found in this setting. The first section of the research explores the background and theory of the two frameworks: First, is the 4-layered hierarchical framework, namely the 4 layers are: 1) Feature extraction, 2) Behavior classification, 3) Individual behavior sequence, and 4) Social interaction. Second, is the Context-Driven Activity Theory, where, we created a complex activity dataset with 12 activities performed by a patient which depicts some very simple and common activities that a patient involves in during the waiting time. We then apply the frameworks in order to validate the performance of the existing works. This study discusses how the results can be particularly beneficial for understanding a patient's experience and make recommendations for improving the quality of patient experience in the United States. Ultimate objectives for such set of collected data and analysis include making work flow in clinics or hospitals more efficient, optimizing office staff functions, and increasing face-to-face time between physicians and patients.

Download or read book Video Tracking written by Emilio Maggio and published by Wiley. This book was released on 2011-02-21 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Video Tracking provides a comprehensive treatment of the fundamental aspects of algorithm and application development for the task of estimating, over time, the position of objects of interest seen through cameras. Starting from the general problem definition and a review of existing and emerging video tracking applications, the book discusses popular methods, such as those based on correlation and gradient-descent. Using practical examples, the reader is introduced to the advantages and limitations of deterministic approaches, and is then guided toward more advanced video tracking solutions, such as those based on the Bayes’ recursive framework and on Random Finite Sets. Key features: Discusses the design choices and implementation issues required to turn the underlying mathematical models into a real-world effective tracking systems. Provides block diagrams and simil-code implementation of the algorithms. Reviews methods to evaluate the performance of video trackers – this is identified as a major problem by end-users. The book aims to help researchers and practitioners develop techniques and solutions based on the potential of video tracking applications. The design methodologies discussed throughout the book provide guidelines for developers in the industry working on vision-based applications. The book may also serve as a reference for engineering and computer science graduate students involved in vision, robotics, human-computer interaction, smart environments and virtual reality programmes

Download or read book Modeling Scenes and Human Activities in Videos written by Arslan Basharat and published by . This book was released on 2009 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, we address the problem of understanding human activities in videos by developing a two-pronged approach: coarse level modeling of scene activities and fine level modeling of individual activities. At the coarse level, where the resolution of the video is low, we rely on person tracks. At the fine level, richer features are available to identify different parts of the human body, therefore we rely on the body joint tracks. There are three main goals of this dissertation: identifying unusual activities at the coarse level, recognizing different activities at the fine level, and predicting the behavior in order to synthesize activities at the fine level. The summary of the three proposed solutions is presented in the following. The first goal is addressed by modeling activities at the coarse level through two novel and complementing approaches. For this purpose, we rely on the tracks of all the moving objects in the scene observed by a static camera. First approach learns the behavior of individuals by modeling the patterns of motion and size of objects in a compact model. The proposed method provides a higher-level process to the traditional real-time surveillance pipeline for identifying unusual activities and feeding back the learned scene model to improve object detection. Pixel level probability density functions (pdfs) of appearance have been used for background modeling in the past, however modeling pixel level pdfs of object speed and size from the tracks is novel. Each pdf is modeled as a multivariate Gaussian Mixture Model (GMM) of the motion (destination location & transition time) and the size (width & height) parameters of the objects at that location. Output of the tracking module is used to perform unsupervised EM-based learning of a GMM at every pixel location. Second approach learns the interaction of object pairs concurrently present in the scene. This can be useful in detecting more complicated activities that the first approach cannot model. We use a higher dimensional Kernel Density Estimation (KDE) model in order to create this model. Mean shift is used for sample refinement followed by Markov Chain during testing stage. The proposed model is successfully used to detect abnormal activities like illegal jaywalking, person drop-off and pickup, etc. Most object path modeling approaches first cluster the tracks into major paths in the scene, which can be a source of error. We avoid this by building local pdfs that capture a variety of tracks which are passing through them. We also show the improvements in object detection through the feedback of the learned scene model. The second and third goals of modeling human activities at the fine level are addressed by employing non-linear dynamical systems. We show that such a model can be useful in recognition and prediction of the underlying dynamics of human activities. In the case of human activities, we use the trajectories of human body joints as the time series data generated by the underlying dynamical system. For this work we have borrowed the relevant key concepts from chaos theory and developed methods to utilize them to solve the problems at hand. Next, we explain the proposed recognition and synthesis methodologies based on the chaotic modeling of human activities. We introduce a recognition framework that uses concepts from the theory of chaotic systems to model nonlinear dynamics of human activities. The observed time series data is used to reconstruct a phase space of appropriate dimension by employing a delay-embedding scheme. The properties of the reconstructed phase space are captured in terms of dynamical and metric invariants, which include the Lyapunov exponent, correlation integral, and correlation dimension. The underlying dynamical system is eventually represented by a composite feature vector containing these invariants. Our contributions in this work include: investigation of the appropriateness of the theory of chaotic systems for human activity modeling and recognition, a new set of features to characterize nonlinear dynamics of human activities, and experimental validation of the feasibility and potential merits of carrying out activity recognition using methods from the theory of chaotic systems. Finally, we also propose a framework for predicting the time series data observed in human activities. We utilize concepts from chaos theory in order to predict the behavior of a nonlinear dynamical system which exhibits deterministic behavior. Observed time series from such a system can be embedded into a higher dimensional phase space without the knowledge of an exact model of the underlying dynamics. Given an initial condition, the predictions in the phase space are computed through kernel regression. This approach has the advantage of modeling dynamics without making any assumptions about the exact form (linear, polynomial, radial basis, etc.) of the mapping function. The predicted points are then warped back to the time series format. We demonstrate the utility of these predictions for human activity synthesis and tracking. Our main contributions are: multivariate phase space reconstruction for human activities, a deterministic approach in contrast to the popular noise-driven approaches, and activity prediction through kernel regression in the phase space.

Download or read book Recognizing Human Activities from Low resolution Videos written by Chia-Chih Chen and published by . This book was released on 2011 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human activity recognition is one of the intensively studied areas in computer vision. Most existing works do not assume video resolution to be a problem due to general applications of interests. However, with continuous concerns about global security and emerging needs for intelligent video analysis tools, activity recognition from low-resolution and low-quality videos has become a crucial topic for further research. In this dissertation, We present a series of approaches which are developed specifically to address the related issues regarding low-level image preprocessing, single person activity recognition, and human-vehicle interaction reasoning from low-resolution surveillance videos. Human cast shadows are one of the major issues which adversely effect the performance of an activity recognition system. This is because human shadow direction varies depending on the time of the day and the date of the year. To better resolve this problem, we propose a shadow removal technique which effectively eliminates a human shadow cast from a light source of unknown direction. A multi-cue shadow descriptor is employed to characterize the distinctive properties of shadows. Our approach detects, segments, and then removes shadows. We propose two different methods to recognize single person actions and activities from low-resolution surveillance videos. The first approach adopts a joint feature histogram based representation, which is the concatenation of subspace projected gradient and optical flow features in time. However, in this problem, the use of low-resolution, coarse, pixel-level features alone limits the recognition accuracy. Therefore, in the second work, we contributed a novel mid-level descriptor, which converts an activity sequence into simultaneous temporal signals at body parts. With our representation, activities are recognized through both the local video content and the short-time spectral properties of body parts' movements. We draw the analogies between activity and speech recognition and show that our speech-like representation and recognition scheme improves recognition performance in several low-resolution datasets. To complete the research on this subject, we also tackle the challenging problem of recognizing human-vehicle interactions from low-resolution aerial videos. We present a temporal logic based approach which does not require training from event examples. At the low-level, we employ dynamic programming to perform fast model fitting between the tracked vehicle and the rendered 3-D vehicle models. At the semantic-level, given the localized event region of interest (ROI), we verify the time series of human-vehicle spatial relationships with the pre-specified event definitions in a piecewise fashion. Our framework can be generalized to recognize any type of human-vehicle interaction from aerial videos.