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Author: Mohd Faid bin Yahya
Publisher:
ISBN:
Category : Video surveillance
Languages : en
Pages : 338
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Book Description
In recent years, off-the-shelf cameras became vastly available, producing a huge amount of content that can be used in various applications. Among the applications, visual surveillance receives a great deal of interest. In visual surveillance, for the purpose of advanced security and monitoring system, human recognition at a distance is of importance. Interest in finding specific individual becomes important such as the case in locating missing person and identification of terrorist from recording of video surveillance in public places. Many methods published so far for human recognition in video image analysis focused on face and gait. However, these methods have low recognition performance due to some external factors such as face are prone to fake facial images and gait is susceptible to occlusion. The presence of the occlusion introduces errors into many existing vision algorithms which have yet to be resolved. In such situations vision techniques to identify a human fails because descriptors of part of the human shape may not have any resemblance with the descriptors of the entire human shape. To resolve this problem, an intelligent system for human recognition under partial occlusion is proposed in this study. The hypothesis of the study is that special features of human body shape can be used to identify a person identity from a distance. Each person has different body features characteristics and hence recognizable using these special body shape features. The body shape features used are the head, shoulder, and trunk. The features can be recognized using fuzzy logic (FL) approach and used as inputs to a recognition system based on a multilayer neural network (MNN). For the developed human recognition algorithm, database has been implemented to provide efficiency in displaying, storing, and retrieval of data. Experimental results show that the developed human recognition system is capable of detecting and recognizing human subject from a distance with 98.1% and 77.5% accuracy respectively. For successful identity recognition, the average percentage coverage of occlusion for Single Direction Partial Occlusion Test (SDPOT) from the top, bottom, left, and right of specific human subjects are 0.41%, 10.41%, 8.44%, and 12.27% respectively. Whereas for Multiple Direction Partial Occlusion Test (MDPOT), an average of 40.15% coverage of occlusions is achieved with successful recognition while the lowest is found to be 11.92% and highest is 60.92%.
Author: Mohd Faid bin Yahya
Publisher:
ISBN:
Category : Video surveillance
Languages : en
Pages : 338
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Book Description
In recent years, off-the-shelf cameras became vastly available, producing a huge amount of content that can be used in various applications. Among the applications, visual surveillance receives a great deal of interest. In visual surveillance, for the purpose of advanced security and monitoring system, human recognition at a distance is of importance. Interest in finding specific individual becomes important such as the case in locating missing person and identification of terrorist from recording of video surveillance in public places. Many methods published so far for human recognition in video image analysis focused on face and gait. However, these methods have low recognition performance due to some external factors such as face are prone to fake facial images and gait is susceptible to occlusion. The presence of the occlusion introduces errors into many existing vision algorithms which have yet to be resolved. In such situations vision techniques to identify a human fails because descriptors of part of the human shape may not have any resemblance with the descriptors of the entire human shape. To resolve this problem, an intelligent system for human recognition under partial occlusion is proposed in this study. The hypothesis of the study is that special features of human body shape can be used to identify a person identity from a distance. Each person has different body features characteristics and hence recognizable using these special body shape features. The body shape features used are the head, shoulder, and trunk. The features can be recognized using fuzzy logic (FL) approach and used as inputs to a recognition system based on a multilayer neural network (MNN). For the developed human recognition algorithm, database has been implemented to provide efficiency in displaying, storing, and retrieval of data. Experimental results show that the developed human recognition system is capable of detecting and recognizing human subject from a distance with 98.1% and 77.5% accuracy respectively. For successful identity recognition, the average percentage coverage of occlusion for Single Direction Partial Occlusion Test (SDPOT) from the top, bottom, left, and right of specific human subjects are 0.41%, 10.41%, 8.44%, and 12.27% respectively. Whereas for Multiple Direction Partial Occlusion Test (MDPOT), an average of 40.15% coverage of occlusions is achieved with successful recognition while the lowest is found to be 11.92% and highest is 60.92%.
Author: W.C.-C. Chu
Publisher: IOS Press
ISBN: 1614994846
Category : Computers
Languages : en
Pages : 2244
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Book Description
This book presents the proceedings of the International Computer Symposium 2014 (ICS 2014), held at Tunghai University, Taichung, Taiwan in December. ICS is a biennial symposium founded in 1973 and offers a platform for researchers, educators and professionals to exchange their discoveries and practices, to share research experiences and to discuss potential new trends in the ICT industry. Topics covered in the ICS 2014 workshops include: algorithms and computation theory; artificial intelligence and fuzzy systems; computer architecture, embedded systems, SoC and VLSI/EDA; cryptography and information security; databases, data mining, big data and information retrieval; mobile computing, wireless communications and vehicular technologies; software engineering and programming languages; healthcare and bioinformatics, among others. There was also a workshop on information technology innovation, industrial application and the Internet of Things. ICS is one of Taiwan's most prestigious international IT symposiums, and this book will be of interest to all those involved in the world of information technology.
Author: Koen Vanhoof
Publisher: World Scientific
ISBN: 9814465704
Category : Computers
Languages : en
Pages : 727
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Book Description
ISKE2009 is the fourth in a series of conferences on Intelligent Systems and Knowledge Engineering. The ISKE2009 proceedings covers state-of-the-art research and development in various areas of Intelligent Systems and Knowledge Engineering, particularly of Intelligent Decision Making Systems.
Author: Bir Bhanu
Publisher: Springer Science & Business Media
ISBN: 0857291246
Category : Computers
Languages : en
Pages : 268
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Book Description
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: Nurul Fatiha Johan
Publisher:
ISBN:
Category : Computer vision
Languages : en
Pages : 218
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Book Description
The application of computer vision in the surveillance system has provided huge advantages in the field of security and safety system. In recent years, human detection and classification subjects have shown an increasing focus in finding specific individual such as in the case of detecting person in crowded places at a time. Detection and classification of human can be a challenging task due to the wide variability of human appearance in terms of clothing, lighting conditions and the occlusion. These constraints directly influence the effectiveness of the overall system. To cope with these problems, human detection and classification system is presented in this thesis which requires fast computations in addition of accurate results. The propose system will first detect the human in an image by using YCbCr color thresholding for skin color detection algorithm and then classify the body parts using artificial intelligent neural network classifier into specific class and finally extend the classification system with the majority voting technique in order to improve the classification performance.The first hypothesis of the research is that YCbCr skin color detection method can be used to detect and identify the exposed human body parts even with the existence of various illumination conditions and complex background. In this work, the body parts then only cover face and hands. The body features are then extracted using feature extraction technique with the dimension of region detected fixed to a standard size.These body features are then used as an input to neural network system in order to classify the body parts into specific class. Meanwhile each class consists of three classifier which is taken from the extracted body regions and separated into face classifier, right hand classifier and left hand classifier. Finally, the results of each body parts classification will be processed using majority voting technique for overall conclusion of the classification system which is robust to partial occlusion. Experimental results indicate that the human detection using YCbCr color space is capable to detect the human body with the percentage of face detection is 92%, right hand detection is 86% and left hand detection is 85%. Meanwhile the performance of ANN classification system is successful in identifying face, right hand and left hand which are 90%, 73% and 74% respectively. Whereas, the accuracy of all 9 classes (Class A until Class I) is found to be 43% and highest to be 95%. Based on the extended classification system using majority voting technique, the results have shown a bit improvement on the classification performance for all 9 classes which is the lowest is increase to 45% and the highest is increase to 100%.
Author: Rama Chellappa
Publisher: Springer Nature
ISBN: 303102236X
Category : Technology & Engineering
Languages : en
Pages : 171
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Book Description
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.
Author: Jiang Wang
Publisher: Springer Science & Business Media
ISBN: 331904561X
Category : Computers
Languages : en
Pages : 65
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Book Description
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: Azhar bin Mohd Ibrahim
Publisher:
ISBN:
Category : Automatic tracking
Languages : en
Pages : 354
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Book Description
In recent years, video surveillance system has emerged as one of the active research area in machine vision community. This thesis intends to integrate machine vision into video surveillance system in order to enhance the accurateness and robustness of video surveillance system. To realize more robust and secure video surveillance system, an automated human and objects' tracking system is needed which can detect, classify and track human and objects even when the occlusion occurs. Hence, we proposed an automated human tracking system which includes detection, classification and tracking of human and objects (especially vehicles) in real-time surveillance system and also in solving the problem of partially occluded human by utilizing fast-computation techniques without compromising the accuracy and performance of that particular surveillance system. In this thesis, we presented the use of foreground segmentation based on adaptive background subtraction to extract foreground objects from the image. Then, to obtain correct detection, we apply shadow removal based on global contrast adjustments in RGB colour space. Next, the extracted foreground objects will be morphologically reconstructed before process of classification. In the process of classification, we utilized new set of affine moment invariants based on statistics method together with aspect ratio in order to classify the extracted foreground objects. Then, finally we track the classified human and objects using feature based tracking for five states, which are: entering, leaving, normal, merging, and splitting. The developed video surveillance system can classify human by extracting the human head-shoulder up to 60 - 70 % occlusion with background objects. Besides that, the developed system also can track the human even if occlusion occurs since we used merging and splitting cases in our tracking algorithm. The overall accuracy for our proposed surveillance system in classifying human and objects (especially car) is excellent which is at 97.51%. The overall accuracy for our surveillance system in tracking human and objects (especially car) is fine also which is at 94.74%.
Author: Zhongxu Hu
Publisher: Springer Nature
ISBN: 981192290X
Category : Computers
Languages : en
Pages : 130
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Book Description
This book offers a systematic, comprehensive, and timely review on V-HAR, and it covers the related tasks, cutting-edge technologies, and applications of V-HAR, especially the deep learning-based approaches. The field of Human Activity Recognition (HAR) has become one of the trendiest research topics due to the availability of various sensors, live streaming of data and the advancement in computer vision, machine learning, etc. HAR can be extensively used in many scenarios, for example, medical diagnosis, video surveillance, public governance, also in human–machine interaction applications. In HAR, various human activities such as walking, running, sitting, sleeping, standing, showering, cooking, driving, abnormal activities, etc., are recognized. The data can be collected from wearable sensors or accelerometer or through video frames or images; among all the sensors, vision-based sensors are now the most widely used sensors due to their low-cost, high-quality, and unintrusive characteristics. Therefore, vision-based human activity recognition (V-HAR) is the most important and commonly used category among all HAR technologies. The addressed topics include hand gestures, head pose, body activity, eye gaze, attention modeling, etc. The latest advancements and the commonly used benchmark are given. Furthermore, this book also discusses the future directions and recommendations for the new researchers.
Author: R. Vinodini
Publisher: A.K. Publications
ISBN: 9786997945180
Category : Computers
Languages : en
Pages : 0
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Book Description
In Our daily life Vision plays a very important role to understand the universe in every aspect. The vision system of our body takes care of taking the information about objects to the brain and recognizes them through the training by the neural system. For this operation the complex design of the body does with very little effort. This creates a advanced feedback and response method between the world and humans. The human vision system has led inventors and engineers to build devices which can visualize like people, analyze and do decision making. The so called Artifical intelligence do more than a normal human do. This helped people to build devices and machines which can make life easier and do difficult tasks easily. The security systems in banking etc were moved from password based security to image processing method. The human machine interface needs the human face recognition system for operation. For security and image processing applications the face detection is the major block which does the main role. In biometric systems the research is focused mainly on the detection of face from a camera image and should be recognized, In crime investigations the process is difficult since the face to be detected and recognized is from a camera which records a video from which the face has to be detected using frame analyzes. The process becomes complex if the image is captured in a bad light or worst climatic conditions. So the design of methods becomes more interesting that verifies the presence of faces from dim or worst images. To overcome the problems this research is focused on new methodologies which can detect and recognize faces. Consider a system which does the pattern-recognition based on the face detection which detects the face and recognizes the face based on the feature vectors of the behavioral or physiological characteristics. The most secure way of accessing banking process is authenticated using biometrics. Since the individual verification is done using a physical and unique biometric characteristic, the conventional passwords or PINs based accessibility frauds can be reduced on corporate computer networks and the Internet because they can be guessed or stolen. Plastic cards, smart cards or computer token cards by themselves are also not secure because they can be forged, stolen or lost, or can become corrupt or unreadable. Biometric methods for identification can be widely adapted to forensics, ATM banking, time and attendance recording, access control and many other applications. The face recognition provides multiple benefits passively over the other methods. But the past methods voluntary action has the disadvantages of being tough to use as well as non adaptable for covert use as in surveillance applications. Human operators are prone to errors during Face image audit and verification during logging biometrics records. Good face images are easier to acquire than good fingerprints. About 5% of people cannot provide a fingerprint for verification. The reasons may be due to dry skin, diseased skin, old skin, cut skin, callused finger, oriental skin, bandaged finger, narrow finger, smudged sensor on reader.
