Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Human Action Recognition In Video Data Using Color And Distance PDF full book. Access full book title Human Action Recognition In Video Data Using Color And Distance.
| Author | : Rawya Al-Akam |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : Color |
| ISBN | : |
| Author | : |
| Publisher | : |
| Total Pages | : |
| Release | : 2005 |
| Genre | : |
| ISBN | : |
| Author | : Bir Bhanu |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 268 |
| Release | : 2010-11-05 |
| Genre | : Computers |
| ISBN | : 0857291246 |
Most biometric systems employed for human recognition require physical contact with, or close proximity to, a cooperative subject. Far more challenging is the ability to reliably recognize individuals at a distance, when viewed from an arbitrary angle under real-world environmental conditions. Gait and face data are the two biometrics that can be most easily captured from a distance using a video camera. This comprehensive and logically organized text/reference addresses the fundamental problems associated with gait and face-based human recognition, from color and infrared video data that are acquired from a distance. It examines both model-free and model-based approaches to gait-based human recognition, including newly developed techniques where the both the model and the data (obtained from multiple cameras) are in 3D. In addition, the work considers new video-based techniques for face profile recognition, and for the super-resolution of facial imagery obtained at different angles. Finally, the book investigates integrated systems that detect and fuse both gait and face biometrics from video data. Topics and features: discusses a framework for human gait analysis based on Gait Energy Image, a spatio-temporal gait representation; evaluates the discriminating power of model-based gait features using Bayesian statistical analysis; examines methods for human recognition using 3D gait biometrics, and for moving-human detection using both color and thermal image sequences; describes approaches for the integration face profile and gait biometrics, and for super-resolution of frontal and side-view face images; introduces an objective non-reference quality evaluation algorithm for super-resolved images; presents performance comparisons between different biometrics and different fusion methods for integrating gait and super-resolved face from video. This unique and authoritative text is an invaluable resource for researchers and graduate students of computer vision, pattern recognition and biometrics. The book will also be of great interest to professional engineers of biometric systems.
| Author | : Jiang Wang |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 65 |
| Release | : 2014-01-25 |
| Genre | : Computers |
| ISBN | : 331904561X |
Action recognition technology has many real-world applications in human-computer interaction, surveillance, video retrieval, retirement home monitoring, and robotics. The commoditization of depth sensors has also opened up further applications that were not feasible before. This text focuses on feature representation and machine learning algorithms for action recognition from depth sensors. After presenting a comprehensive overview of the state of the art, the authors then provide in-depth descriptions of their recently developed feature representations and machine learning techniques, including lower-level depth and skeleton features, higher-level representations to model the temporal structure and human-object interactions, and feature selection techniques for occlusion handling. This work enables the reader to quickly familiarize themselves with the latest research, and to gain a deeper understanding of recently developed techniques. It will be of great use for both researchers and practitioners.
| Author | : Weilong Yang |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2010 |
| Genre | : Computer vision |
| ISBN | : |
In this thesis, we address an important topic in computer vision, human action recognition and detection. In particular, we focus on a special scenario where only a single clip is available for training for each action category. This is a very natural scenario in many real-world applications, such as video search and intelligent video surveillance. We present a transfer learning technique called transferable distance function learning and apply it in human action recognition and detection. This learning algorithm aims to extract generic knowledge from previous training sets, and apply this knowledge to videos of new actions without further learning. It is experimentally demonstrated that the proposed algorithm can improve the accuracy of single clip action recognition and detection. Based on the learned transferable distance function, we further propose a cascade structure which can significantly improve the efficiency of an action detection system.
| Author | : Chaitanya Gurrapu |
| Publisher | : |
| Total Pages | : 107 |
| Release | : 2004 |
| Genre | : Gesture |
| ISBN | : |
| Author | : A. Ranjith Ram |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 185 |
| Release | : 2012-06-13 |
| Genre | : Computers |
| ISBN | : 1461438373 |
This book presents various video processing methodologies that are useful for distance education. The motivation is to devise new multimedia technologies that are suitable for better representation of instructional videos by exploiting the temporal redundancies present in the original video. This solves many of the issues related to the memory and bandwidth limitation of lecture videos. The various methods described in the book focus on a key-frame based approach which is used to time shrink, repackage and retarget instructional videos. All the methods need a preprocessing step of shot detection and recognition, which is separately given as a chapter. We find those frames which are well-written and distinct as key-frames. A super-resolution based image enhancement scheme is suggested for refining the key-frames for better legibility. These key-frames, along with the audio and a meta-data for the mutual linkage among various media components form a repackaged lecture video, which on a programmed playback, render an estimate of the original video but at a substantially compressed form. The book also presents a legibility retentive retargeting of this instructional media on mobile devices with limited display size. All these technologies contribute to the enhancement of the outreach of distance education programs. Distance education is now a big business with an annual turnover of over 10-12 billion dollars. We expect this to increase rapidly. Use of the proposed technology will help deliver educational videos to those who are less endowed in terms of network bandwidth availability and to those everywhere who are even on a move by delivering it effectively to mobile handsets (including PDAs). Thus, technology developers, practitioners, and content providers will find the material very useful.
| Author | : Xue-Bo Jin |
| Publisher | : MDPI |
| Total Pages | : 602 |
| Release | : 2020-03-23 |
| Genre | : Technology & Engineering |
| ISBN | : 3039283022 |
This book includes papers from the section “Multisensor Information Fusion”, from Sensors between 2018 to 2019. It focuses on the latest research results of current multi-sensor fusion technologies and represents the latest research trends, including traditional information fusion technologies, estimation and filtering, and the latest research, artificial intelligence involving deep learning.
| Author | : Dey, Nilanjan |
| Publisher | : IGI Global |
| Total Pages | : 321 |
| Release | : 2016-10-11 |
| Genre | : Computers |
| ISBN | : 1522510230 |
Video monitoring has become a vital aspect within the global society as it helps prevent crime, promote safety, and track daily activities such as traffic. As technology in the area continues to improve, it is necessary to evaluate how video is being processed to improve the quality of images. Applied Video Processing in Surveillance and Monitoring Systems investigates emergent techniques in video and image processing by evaluating such topics as segmentation, noise elimination, encryption, and classification. Featuring real-time applications, empirical research, and vital frameworks within the field, this publication is a critical reference source for researchers, professionals, engineers, academicians, advanced-level students, and technology developers.
| Author | : Haoran Wei |
| Publisher | : |
| Total Pages | : |
| Release | : 2020 |
| Genre | : Human activity recognition |
| ISBN | : |
This dissertation addresses deep learning solutions for two applications. The first application involves performing continuous human action recognition by simultaneous utilization of inertial and video sensing. The objective in this application is to achieve a more robust continuous action recognition compared to using a single sensing modality by simultaneously utilizing a video camera and a wearable inertial sensor. A deep learning solution is developed that differs from the action recognition approaches reported in the literature in two ways: (i) The detection and recognition of actions are carried out for continuous action streams and not on segmented actions, which is the assumption normally made in existing action recognition approaches. (ii) It provides the first attempt at using video and inertial sensing together or simultaneously in order to achieve continuous action recognition. As part of this effort, a Continuous Multimodal Human Action Dataset (named C-MHAD) is collected and made publicly available. The second application involves detecting persons and the load they carry in far field video surveillance data. The objective in this application is to detect persons and to classify the load carried by them from video data captured from distances several miles away via high-power lens video cameras. A deep learning solution is developed to cope with the following two major challenges: (i) Far field video data suffer from various noises caused by wind, heat haze, and the camera being out of focus thus generating blurriness of persons appearing in video images. (ii) The available dataset is small and lack no frame-level labels. The results obtained indicate the effectiveness of the developed deep learning solutions.
