Distributed Smart Cameras Research Articles

Machine Learning Based Performance Analysis of Video Object Detection and Classification Using Modified Yolov3 and Mobilenet Algorithm

Detecting foreground objects in video is crucial in various machine vision applications and computerized video surveillance technologies. Object tracking and detection are essential in object identification, surveillance, and navigation approaches. Object detection is the technique of differentiating between background and foreground features in a photograph. Recent improvements in vision systems, including distributed smart cameras, have inspired researchers to develop enhanced machine vision applications for embedded systems. The efficiency of featured object detection algorithms declines as dynamic video data increases as contrasted to conventional object detection methods. Moving subjects that are blurred, fast-moving objects, backdrop occlusion, or dynamic background shifts within the foreground area of a video frame can all cause problems. These challenges result in insufficient prominence detection. This work develops a deep-learning model to overcome this issue. For object detection, a novel method utilizing YOLOv3 and MobileNet was built. First, rather than picking predefined feature maps in the conventional YOLOv3 architecture, the technique for determining feature maps in the MobileNet is optimized based on examining the receptive fields. This work focuses on three primary processes: object detection, recognition, and classification, to classify moving objects before shared features. Compared to existing algorithms, experimental findings on public datasets and our dataset reveal that the suggested approach achieves 99% correct classification accuracy for urban settings with moving objects. Experiments reveal that the suggested model beats existing cutting-edge models by speed and computation.

Development of integrated optoelectronic system for search and rescue mission control

In this work, the results of development of optoelectronic system designed for search and rescue maritime operation are presented. Due to integration a number of few data sources in the console of the system operator, the main focus of the paper is signal interfaces and data flow within the developed solution. The structure of the system is described as well as data flow for images and control is presented. Finally, the images showing examples of system operation are shown. Full Text: PDF References G. Bieszczad, T. Sosnowski, H. Madura, M. Kastek, J. Barela, Image processing module for high-speed thermal camera with cooled detector, Proc. SPIE 8012, Technology and Applications XXXVII 80120L (2011). CrossRef N.K. Dhar, R. Dat, A.K. Sood in Optoelectronics - Advanced Materials and Devices, S. Pyshkin (Ed.), InTech (2013). DirectLink O.W. Ibraheem, A. Irwansyah, J. Hagemeyer, M. Porrmann, U. Rueckert, 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig), 1 (2015). DirectLink K.D. Nibir, D. Ravi, K.S. Ashok in Optoelectronics - Advanced Materials and Devices, S. Pyshkin (Ed.), InTech (2013); available from: http://www.intechopen.com/books/optoelectronics-advanced-materials-and-devices/advances-in-infrared-detector-array-technology DirectLink E. Norouznezhad, A. Bigdeli, A. Postula, B.C. Lovell, high resolution smart camera with GigE Vision extension for surveillance applications, ACM/IEEE Second International Conference on Distributed Smart Cameras ICDSC 2008, 1 (2008). CrossRef A. Rogalski, Progress in focal plane array technologies, Prog. Quantum Electron. 36(2), 342 (2012). CrossRef A. Rogalski, Infrared Detectors for the Future, Acta Phys. Polonica A 116(3), 389 (2009). CrossRef A. Rogalski, Semiconductor detectors and focal plane arrays for far-infrared imaging, Opto-Electr. Review 21(4), 406 (2013). CrossRef T. Sprafke, J.W. Beletic, High-Performance Focal Plane Arrays for Space Applications, Optics & Photon. News 19, 22 (2008). CrossRef J. Stachiw, D. Endicott, Material and Design Considerations for Thermal Imager Windows in Marine Service, Oceans '79, 424 (1979). CrossRef W. Yuan He, K.H. Xiao, Z. Xu, high speed robot vision system with GigE vision extension, IEEE International Conference on Mechatronics and Automation, 452 (2011). CrossRef

Special Section Guest Editorial: Advances on Distributed Smart Cameras

This PDF file contains the editorial “Special Section Guest Editorial:Advances on Distributed Smart Cameras” for JEI Vol. 25 Issue 04

A Genetic Algorithm-Based Moving Object Detection for Real-time Traffic Surveillance

Recent developments in vision systems such as distributed smart cameras have encouraged researchers to develop advanced computer vision applications suitable to embedded platforms. In the embedded surveillance system, where memory and computing resources are limited, simple and efficient computer vision algorithms are required. In this letter, we present a moving object detection method for real-time traffic surveillance applications. The proposed method is a combination of a genetic dynamic saliency map (GDSM), which is an improved version of dynamic saliency map (DSM) and background subtraction. The experimental results show the effectiveness of the proposed method in detecting moving objects.

Octree-Based 3D Logic and Computation of Spatial Relationships in Live Video Query Processing

Live video computing (LVC) on distributed smart cameras has many important applications; and a database approach based on a Live Video DataBase Management System (LVDBMS) has shown to be effective for general LVC application development. The performance of such a database system relies on accurate interpretation of spatial relationships among objects in the live video. With the popularity of affordable depth cameras, 3D spatial computation techniques have been applied. However, the 3D object models currently used are expensive to compute, and offer limited scalability. We address this drawback in this article by proposing an octree-based 3D spatial logic and presenting algorithms for computing 3D spatial relationships using depth cameras. To support continuous query processing on live video streams, we also develop a GPU-based implementation of the proposed technique to further enhance scalability for real-time applications. Extensive performance studies based on a public RGB-D dataset as well as the LVDBMS prototype demonstrates the correctness and efficiency of our techniques.

Visual re-identification across large, distributed camera networks

We propose a holistic approach to the problem of re-identification in an environment of distributed smart cameras. We model the re-identification process in a distributed camera network as a distributed multi-class classifier, composed of spatially distributed binary classifiers. We treat the problem of re-identification as an open-world problem, and address novelty detection and forgetting. As there are many tradeoffs in design and operation of such a system, we propose a set of evaluation measures to be used in addition to the recognition performance. The proposed concept is illustrated and evaluated on a new many-camera surveillance dataset and SAIVT-SoftBio dataset.

Single authentication through in convergence space using collaborative smart cameras

AbstractIn recent years, the convergence of IT space and physical space is increasingly studied. In the legacy IT‐based systems, developments of services were focusing on just the cyber space. However, as ubiquitous computing environment is expanding into the real world, considerations about how to design and develop the systems for ensuring the interoperability between two spaces must be taken. For indeed converging IT/physical spaces and ensuring the ubiquity, a new model to efficiently identify a moving object needs to be established. Although the identifier information resulted from successful authentication procedure is used in the most security systems, each authentication method adopts a variety of identifiable information (II) specification. So in this paper, we suggest a scheme to access any ubiquitous service with single authentication at initial stage for efficiently identifying an object moving multiple convergence spaces by relaying the II along the movement. This is performed by enabling distributed smart cameras to deliver II of the identified moving object. Copyright © 2014 John Wiley & Sons, Ltd.

Socio-economic vision graph generation and handover in distributed smart camera networks

In this article we present an approach to object tracking handover in a network of smart cameras, based on self-interested autonomous agents, which exchange responsibility for tracking objects in a market mechanism, in order to maximise their own utility. A novel ant-colony inspired mechanism is used to learn the vision graph, that is, the camera neighbourhood relations, during runtime, which may then be used to optimise communication between cameras. The key benefits of our completely decentralised approach are on the one hand generating the vision graph online, enabling efficient deployment in unknown scenarios and camera network topologies, and on the other hand relying only on local information, increasing the robustness of the system. Since our market-based approach does not rely on a priori topology information, the need for any multicamera calibration can be avoided. We have evaluated our approach both in a simulation study and in network of real distributed smart cameras.

System and software architectures of distributed smart cameras

In this article, we describe a distributed, peer-to-peer gesture recognition system along with a software architecture modeling technique and authority control protocol for ubiquitous cameras. This system performs gesture recognition in real time by combining imagery from multiple cameras without using a central server. We propose a system architecture that uses a network of inexpensive cameras to perform in-network video processing. A methodology for transforming well-designed single-node algorithm to distributed system is also proposed. Applications for ubiquitous cameras can be modeled as the composition of a finite-state machine of the system, functional services, and middleware. A service-oriented software architecture is proposed to dynamically reconfigure services when system state changes. By exchanging data and control messages between neighboring sensors, each node can maintain broader view of the environment with integrated video-processing results. Our prototype system is built on Windows machines, and uses standard video cameras as sensors and local network as a communication channel.

A novel software framework for embedded multiprocessor smart cameras

Distributed smart cameras (DSC) are an emerging technology for a broad range of important applications including smart rooms, surveillance, entertainment, tracking, and motion analysis. By having access to many views and through cooperation among the individual cameras, these DSCs have the potential to realize many more complex and challenging applications than single-camera systems. This article focuses on the system-level software required for efficient streaming applications on single smart cameras as well as on networks of DSCs. Embedded platforms with limited resources do not provide middleware services well known on general-purpose platforms. Our software framework supports transparent intra- and interprocessor communication while keeping the memory and computation overhead very low. The software framework is based on a publisher--subscriber architecture and provides mechanisms for dynamically loading and unloading software components as well as for graceful degradation in case of software- and hardware-related faults. The software framework has been completely implemented and tested on our embedded smart cameras consisting of an ARM-based network processor and several digital signal processors. Two case studies demonstrate the feasibility of our approach.

An Introduction to Distributed Smart Cameras

Distributed smart cameras (DSCs) are real-time distributed embedded systems that perform computer vision using multiple cameras. This new approach has emerged thanks to a confluence of simultaneous advances in four key disciplines: computer vision, image sensors, embedded computing, and sensor networks. Processing images in a network of distributed smart cameras introduces several complications. However, we believe that the problems DSCs solve are much more important than the challenges of designing and building a distributed video system. We argue that distributed smart cameras represent key components for future embedded computer vision systems and that smart cameras will become an enabling technology for many new applications. We summarize smart camera technology and applications, discuss current trends, and identify important research challenges.

Security and Privacy in Distributed Smart Cameras

Distributed smart camera systems are becoming increasingly important in a wide range of applications. As they are often deployed in public space and/or our personal environment, they increasingly access and manipulate sensitive or private information. Their architectures need to address security and privacy issues appropriately, considering them from the inception of the overall system structure. In this paper, we present security and privacy issues of distributed smart camera systems. We describe security requirements, possible attacks, and common risks, analyzing issues at the node and at the network level and presenting available solutions. Although security issues of distributed smart cameras are analogous to networked embedded systems and sensor networks, emphasis is given to special requirements of smart camera networks, including privacy and continuous real-time operation.

Special issue on Distributed Smart Cameras - Table of contents

Special issue on Distributed Smart Cameras - Table of contents

A Bright Future for Distributed Smart Cameras

The 11 articles in this special issue on distributed smart cameras are divided into the following three categories: architecture and networks; distributed and collaborative image processing; and applications and security issues.

The Signal Passing Interface and Its Application to Embedded Implementation of Smart Camera Applications

Embedded smart camera systems comprise computation- and resource-hungry applications implemented on small, complex but resource-hardy platforms. Efficient implementation of such applications can benefit significantly from parallelization. However, communication between different processing units is a nontrivial task. In addition, new and emerging distributed smart cameras require efficient methods of communication for optimized distributed implementations. In this paper, a novel communication interface, called the signal passing interface (SPI), is presented that attempts to overcome this challenge by integrating relevant properties of two different, yet important, paradigms in this context-dataflow and message passing interface (MPI). Dataflow is a widely used modeling paradigm for signal processing applications, while MPI is an established communication interface in the general-purpose processor community. SPI is targeted toward computation-intensive signal processing applications, and due to its careful specialization, more performance-efficient for embedded implementation in this domain. SPI is also much easier and more intuitive to use. In this paper, successful application of this communication interface to two smart camera applications has been presented in detail to validate a new methodology for efficient distributed implementation for this domain.

Distributed Multilevel Data Fusion for Networked Embedded Systems

Recently much research has been conducted in visual sensor networks. Compared to traditional sensor networks, vision networks differ in various aspects such as the amount of data to be processed and transmitted, the requirements on quality-of-service, and the level of collaboration among the sensor nodes. This paper deals with sensor fusion on visual sensor networks. We focus here on methods for fusing data from various distributed sensors and present a generic framework for fusion on embedded sensor nodes. This paper extends our previous work on distributed smart cameras and presents our approach toward the transformation of smart cameras into a distributed, embedded multisensor network. Our generic fusion model has been completely implemented on a distributed embedded system. It provides a middleware which supports automatic mapping of our fusion model to the target hardware. This middleware features dynamic reconfiguration to support modification of the fusion application at runtime without loss of sensor data. The feasibility and reusability of the I-SENSE concept is demonstrated with experimental results of two case studies: vehicle classification and bulk good separation. Qualitative and quantitative benefits of multilevel information fusion are outlined in this article.