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Abstract: The main purpose of this work is to implement a new framework for the detection of activities based on the temporal difference method. This system mainly consists of a unique interface with an integrated camera and microphone, for the purpose of monitoring moving objects and sound respectively. The proposed system also detracts one common flaw in motion detection based on the frame differencing technique, with the fusion of background subtraction technique and frame difference method. With the ever increasingly heightened sense of safety consciousness in today's society, video surveillance systems have been widely used in several fields, such as military affairs, public space security monitoring, and even in some private homes. The detection of the occurrence of activities is the most basic and important part of video surveillance systems, as such its quality and robustness warrant special attention and continuous research. The proposed system was implemented using MATLAB 7.10.0, and the results are found to be effective and robust.
This work proposes a complete sensor-independent visual system that provides robust target motion detection. First, the way sensors obtain images, in terms of resolution distribution and pixel neighbourhood, is studied. This allows a spatial analysis of motion to be carried out. Then, a novel background maintenance approach for robust target motion detection is implemented. Two different situations are considered: a fixed camera observing a constant background where objects are moving; and a still camera observing objects in movement within a dynamic background. This distinction lies on developing a surveillance mechanism without the constraint of observing a scene free of foreground elements for several seconds when a reliable initial background model is obtained, as that situation cannot be guaranteed when a robotic system works in an unknown environment. Other problems are also addressed to successfully deal with changes in illumination, and the distinction between foreground and background elements.
This book shows how machine learning can detect moving objects in a digital video stream. The authors present different background subtraction approaches, foreground segmentation, and object tracking approaches to accomplish this. They also propose an algorithm that considers a multimodal background subtraction approach that can handle a dynamic background and different constraints. The authors show how the proposed algorithm is able to detect and track 2D & 3D objects in monocular sequences for both indoor and outdoor surveillance environments and at the same time, also able to work satisfactorily in a dynamic background and with challenging constraints. In addition, the shows how the proposed algorithm makes use of parameter optimization and adaptive threshold techniques as intrinsic improvements of the Gaussian Mixture Model. The presented system in the book is also able to handle partial occlusion during object detection and tracking. All the presented work and evaluations were carried out in offline processing with the computation done by a single laptop computer with MATLAB serving as software environment.
The theme of CUTE is focused on the various aspects of ubiquitous computing for advances in ubiquitous computing and provides an opportunity for academic and industry professionals to discuss the latest issues and progress in the area of ubiquitous computing. Therefore this book will be include the various theories and practical applications in ubiquitous computing
This Springer Brief presents a comprehensive survey of the existing methodologies of background subtraction methods. It presents a framework for quantitative performance evaluation of different approaches and summarizes the public databases available for research purposes. This well-known methodology has applications in moving object detection from video captured with a stationery camera, separating foreground and background objects and object classification and recognition. The authors identify common challenges faced by researchers including gradual or sudden illumination change, dynamic backgrounds and shadow and ghost regions. This brief concludes with predictions on the future scope of the methods. Clear and concise, this brief equips readers to determine the most effective background subtraction method for a particular project. It is a useful resource for professionals and researchers working in this field.
Recent years have seen a vast development in various methodologies for object detection and feature extraction and recognition, both in theory and in practice. When processing images, videos, or other types of multimedia, one needs efficient solutions to perform fast and reliable processing. Computational intelligence is used for medical screening where the detection of disease symptoms is carried out, in prevention monitoring to detect suspicious behavior, in agriculture systems to help with growing plants and animal breeding, in transportation systems for the control of incoming and outgoing transportation, for unmanned vehicles to detect obstacles and avoid collisions, in optics and materials for the detection of surface damage, etc. In many cases, we use developed techniques which help us to recognize some special features. In the context of this innovative research on computational intelligence, the Special Issue “Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments” present an excellent opportunity for the dissemination of recent results and achievements for further innovations and development. It is my pleasure to present this collection of excellent contributions to the research community. - Prof. Marcin Woźniak, Silesian University of Technology, Poland –
Make the most of the common architectures used for deriving meaningful data from sensors. This book provides you with the tools to understand how sensor data is converted into actionable knowledge and provides tips for in-depth work in this field. Making Sense of Sensors starts with an overview of the general pipeline to extract meaningful data from sensors. It then dives deeper into some commonly used sensors and algorithms designed for knowledge extraction. Practical examples and pointers to more information are used to outline the key aspects of Multimodal recognition. The book concludes with a discussion on relationship extraction, knowledge representation, and management. In today’s world we are surrounded by sensors collecting various types of data about us and our environments. These sensors are the primary input devices for wearable computers, IoT, and other mobile devices. The information is presented in way that allows readers to associate the examples with their daily lives for better understanding of the concepts. What You'll Learn Look at the general architecture for sensor based data Understand how data from common domains such as inertial, visual and audio is processed Master multi-modal recognition using multiple heterogeneous sensors Transition from recognition to knowledge through relationship understanding between entities Leverage different methods and tools for knowledge representation and management Who This Book Is For New college graduates and professionals interested in acquiring knowledge and the skills to develop innovative solutions around today's sensor-rich devices.
IoT Fundamentals with a Practical Approach is an insightful book that serves as a comprehensive guide to understanding the foundations and key concepts of Internet of Things (IoT) technologies. The book begins by introducing readers to the concept of IoT, explaining the significance and potential impact on various industries and domains. It covers the underlying principles of IoT, including its architecture, connectivity, and communication protocols, providing readers with a solid understanding of how IoT systems are structured and how devices interact within an IoT ecosystem. This book dives into the crucial components that form the backbone of IoT systems. It explores sensors and actuators, explaining their roles in collecting and transmitting data from the physical environment. The book also covers electronic components used in IoT devices, such as microcontrollers, communication modules, and power management circuits. This comprehensive understanding of the building blocks of IoT allows readers to grasp the technical aspects involved in developing IoT solutions. Security is a vital aspect of IoT, and the book dedicates a significant portion to exploring security challenges and best practices in IoT deployments. It delves into topics such as authentication, encryption, access control, and secure firmware updates, providing readers with essential insights into safeguarding IoT systems against potential threats and vulnerabilities. This book also addresses the scalability and interoperability challenges of IoT. It discusses IoT platforms and frameworks that facilitate the development and management of IoT applications, highlighting their role in enabling seamless integration and communication between devices and systems. The book is written in a clear and accessible manner and includes real-world examples, making it suitable for both beginners and professionals looking to enhance their understanding of IoT. It serves as a valuable resource for engineers, developers, researchers, and decision-makers involved in IoT projects and provides them with the knowledge and tools necessary to design, implement, and secure IoT solutions.
Abstract: "This thesis presents a method for incrementally recognizing objects as they are scanned by range sensors mounted on a mobile platform, such as a construction, mining, or agricultural field robot. The method enhances the productivity of field robotic machines in these settings by allowing them to start planning and moving toward the object before scanning is complete, or execute other motion tasks without the need to stop and scan. The system consists of two components, an on-line method which accomplishes the recognition and an off-line method which generates a finite state machine and associated parameters which guide the process of incremental recognition. The online method handles range data from laser or radar range sensors and is robust to the noise and poor sensor data that can result from unmeasured sensor motion during scanning. The off-line method uses range data obtained by simulating a range sensor scanning the object model in a sequence of poses. The on-line component of the system is used by an automated machine to recognize and locate objects it must interact with in its work area. Since this method handles unmeasured sensor motion, costs of these automated systems can be reduced by eliminating the need for highly accurate positioning systems to compensate for motion during scanning. Objects that can be recognized and localized with this method may consist of planar surface patches that meet at boundaries or planar surface patches with dangling boundaries. Object surfaces and boundaries can contain variations found in industrial objects such as structural ribbing, brackets, or material clinging to the object. Material placed into the object may occlude part of the object's surfaces. The object models are stored as wire-frame models with linear segments corresponding to the boundaries of the surface patches. Additional information incorporates assumptions about the pose of an object and is referenced to the object model. The method continuously reports the best set of matches of object model features to scene model features as the sensor data is received. For the purposes of conducting experiments and evaluating the results this thesis focuses on a specific instance of this problem, the recognition of objects used during excavation operations such as on-highway and off-highway trucks. Results are presented using range data from scanning laser and radar range sensors designed for the environment and tasks of large mobile equipment. Results are presented which show that with a single truck model the method can report incremental descriptions at a rate of 20 Hz. This method has been used in demonstrations in which a hydraulic excavator equipped with range sensors and on-board computing autonomously loads multiple trucks."