172 research outputs found

    Autonomous Multicamera Tracking on Embedded Smart Cameras

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    There is currently a strong trend towards the deployment of advanced computer vision methods on embedded systems. This deployment is very challenging since embedded platforms often provide limited resources such as computing performance, memory, and power. In this paper we present a multicamera tracking method on distributed, embedded smart cameras. Smart cameras combine video sensing, processing, and communication on a single embedded device which is equipped with a multiprocessor computation and communication infrastructure. Our multicamera tracking approach focuses on a fully decentralized handover procedure between adjacent cameras. The basic idea is to initiate a single tracking instance in the multicamera system for each object of interest. The tracker follows the supervised object over the camera network, migrating to the camera which observes the object. Thus, no central coordination is required resulting in an autonomous and scalable tracking approach. We have fully implemented this novel multicamera tracking approach on our embedded smart cameras. Tracking is achieved by the well-known CamShift algorithm; the handover procedure is realized using a mobile agent system available on the smart camera network. Our approach has been successfully evaluated on tracking persons at our campus

    Autonomous real-time surveillance system with distributed IP cameras

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    An autonomous Internet Protocol (IP) camera based object tracking and behaviour identification system, capable of running in real-time on an embedded system with limited memory and processing power is presented in this paper. The main contribution of this work is the integration of processor intensive image processing algorithms on an embedded platform capable of running at real-time for monitoring the behaviour of pedestrians. The Algorithm Based Object Recognition and Tracking (ABORAT) system architecture presented here was developed on an Intel PXA270-based development board clocked at 520 MHz. The platform was connected to a commercial stationary IP-based camera in a remote monitoring station for intelligent image processing. The system is capable of detecting moving objects and their shadows in a complex environment with varying lighting intensity and moving foliage. Objects moving close to each other are also detected to extract their trajectories which are then fed into an unsupervised neural network for autonomous classification. The novel intelligent video system presented is also capable of performing simple analytic functions such as tracking and generating alerts when objects enter/leave regions or cross tripwires superimposed on live video by the operator

    A socio-economic approach to online vision graph generation and handover in distributed smart camera networks

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    Abstract—In this paper we propose an approach based on selfinterested autonomous cameras, 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 grow the vision graph 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 which permits the addition and removal cameras to the network during runtime 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. Index Terms—Smart camera networks; multi-camera tracking; market-based control; topology identification; ant algorithms. I

    Scalable software architecture for on-line multi-camera video processing

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    In this paper we present a scalable software architecture for on-line multi-camera video processing, that guarantees a good trade oïŹ€ between computational power, scalability and ïŹ‚exibility. The software system is modular and its main blocks are the Processing Units (PUs), and the Central Unit. The Central Unit works as a supervisor of the running PUs and each PU manages the acquisition phase and the processing phase. Furthermore, an approach to easily parallelize the desired processing application has been presented. In this paper, as case study, we apply the proposed software architecture to a multi-camera system in order to eïŹƒciently manage multiple 2D object detection modules in a real-time scenario. System performance has been evaluated under diïŹ€erent load conditions such as number of cameras and image sizes. The results show that the software architecture scales well with the number of camera and can easily works with diïŹ€erent image formats respecting the real time constraints. Moreover, the parallelization approach can be used in order to speed up the processing tasks with a low level of overhea

    Improved adaptivity and robustness in decentralised multi-camera networks

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    In this paper we present increased adaptivity and robustness in distributed object tracking by multi-camera networks using a socio-economic mechanism for learning the vision graph. To build-up the vision graph autonomously within a distributed smart-camera network, we use an ant-colony inspired mechanism, which exchanges responsibility for tracking objects using Vickrey auctions. Employing the learnt vision graph allows the system to optimise its communication continuously. Since distributed smart camera networks are prone to uncertainties in individual cameras, such as failures or changes in extrinsic parameters, the vision graph should be sufficiently robust and adaptable during runtime to enable seamless tracking and optimised communication. To better reflect real smart-camera platforms and networks, we consider that communication and handover are not instantaneous, and that cameras may be added, removed or their properties changed during runtime. Using our dynamic socio-economic approach, the network is able to continue tracking objects well, despite all these uncertainties, and in some cases even with improved performance. This demonstrates the adaptivity and robustness of our approach

    Omnidirectional video stabilisation on a virtual camera using sensor fusion

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    This paper presents a method for robustly stabilising omnidirectional video given the presence of significantrotations and translations by creating a virtual camera and using a combination of sensor fusion and scene tracking. Real time rotational movements of the camera are measured by an Inertial Measurement Unit (IMU), which provides an initial estimate of the ego-motion of the camera platform. Image registration is then used to refine these estimates. The calculated ego-motion is then used to adjust an extract of the omnidirectional video, forming a virtual camera that is focused on the scene. Experiments show the technique is effective under challenging ego-motions and overcomes deficiencies that are associated with unimodal approaches making it robust and suitable to be used in many surveillance applications

    Comprehensive Survey and Analysis of Techniques, Advancements, and Challenges in Video-Based Traffic Surveillance Systems

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    The challenges inherent in video surveillance are compounded by a several factors, like dynamic lighting conditions, the coordination of object matching, diverse environmental scenarios, the tracking of heterogeneous objects, and coping with fluctuations in object poses, occlusions, and motion blur. This research endeavor aims to undertake a rigorous and in-depth analysis of deep learning- oriented models utilized for object identification and tracking. Emphasizing the development of effective model design methodologies, this study intends to furnish a exhaustive and in-depth analysis of object tracking and identification models within the specific domain of video surveillance

    Video Analysis in Pan-Tilt-Zoom Camera Networks

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    Multi-camera Control and Video Transmission Architecture for Distributed Systems

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    Proceedings of: Workshop on User-Centric Technologies and Applications (CONTEXTS 2011)The increasing number of autonomous systems monitoring and controlling visual sensor networks, make it necessary an homogeneous (deviceindependent), flexible (accessible from various places), and efficient (real-time) access to all their underlying video devices. This paper describes an architecture for camera control and video transmission in a distributed system like existing in a cooperative multi-agent video surveillance scenario. The proposed system enables the access to a limited-access resource (video sensors) in an easy, transparent and efficient way both for local and remote processes. It is particularly suitable for Pan-Tilt-Zoom (PTZ) cameras in which a remote control is essential.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI,CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM CONTEXTS S2009/TIC-1485 and DPS2008-07029-C02-02.Publicad
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