347 research outputs found

    Information theoretic sensor management for multi-target tracking with a single pan-tilt-zoom camera

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    Automatic multiple target tracking with pan-tilt-zoom (PTZ) cameras is a hard task, with few approaches in the lit-erature, most of them proposing simplistic scenarios. In this paper, we present a PTZ camera management framework which lies on information theoretic principles: at each time step, the next camera pose (pan, tilt, focal length) is chosen, according to a policy which ensures maximum information gain. The formulation takes into account occlusions, phys-ical extension of targets, realistic pedestrian detectors and the mechanical constraints of the camera. Convincing com-parative results on synthetic data, realistic simulations and the implementation on a real video surveillance camera val-idate the effectiveness of the proposed method. 1

    Dynamic Reconfiguration in Camera Networks: A Short Survey

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    There is a clear trend in camera networks towards enhanced functionality and flexibility, and a fixed static deployment is typically not sufficient to fulfill these increased requirements. Dynamic network reconfiguration helps to optimize the network performance to the currently required specific tasks while considering the available resources. Although several reconfiguration methods have been recently proposed, e.g., for maximizing the global scene coverage or maximizing the image quality of specific targets, there is a lack of a general framework highlighting the key components shared by all these systems. In this paper we propose a reference framework for network reconfiguration and present a short survey of some of the most relevant state-of-the-art works in this field, showing how they can be reformulated in our framework. Finally we discuss the main open research challenges in camera network reconfiguration

    Non-myopic information theoretic sensor management of a single pan\u2013tilt\u2013zoom camera for multiple object detection and tracking

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    Detailed derivation of an information theoretic framework for real PTZ management.Introduction and implementation of a non-myopic strategy.Large experimental validation, with synthetic and realistic datasets.Working demonstration of myopic strategy on an off-the-shelf PTZ camera. Automatic multiple object tracking with a single pan-tilt-zoom (PTZ) cameras is a hard task, with few approaches in the literature, most of them proposing simplistic scenarios. In this paper, we present a novel PTZ camera management framework in which at each time step, the next camera pose (pan, tilt, focal length) is chosen to support multiple object tracking. The policy can be myopic or non-myopic, where the former analyzes exclusively the current frame for deciding the next camera pose, while the latter takes into account plausible future target displacements and camera poses, through a multiple look-ahead optimization. In both cases, occlusions, a variable number of subjects and genuine pedestrian detectors are taken into account, for the first time in the literature. Convincing comparative results on synthetic data, realistic simulations and real trials validate our proposal, showing that non-myopic strategies are particularly suited for a PTZ camera management

    Distributed Active-Camera Control Architecture Based on Multi-Agent Systems

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    Proceedings of: 10th Conference on Practical Applications of Agents and Multi-Agent Systems, Salamanca (Spain), 28-30 March 2012 (PAAMS`12)In this contribution a Multi-Agent System architecture is proposed to deal with the management of spatially distributed heterogeneous nets of sensors, specially is described the problem of Pan-Tilt-Zoom or active cameras. The design of surveillance multi-sensor systems implies undertaking to solve two related problems: data fusion and coordinated sensor-task management. Generally, proposed architectures for the coordinated operation of multiple sensors are based on centralization of management decisions at the fusion center. However, the existence of intelligent sensors capable of taking decisions brings the possibility of conceiving alternative decentralized architectures. This problem could be approached by means of a Multi-Agent System (MAS). In specific, this paper proposes a MAS architecture for automatically control sensors in video surveillance environments.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/ TIC-1485) and DPS2008- 07029-C02-02.Publicad

    Toward Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR)

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    Currently, most land Intelligence, Surveillance and Reconnaissance (ISR) assets (e.g. EO/IR cameras) are simply data collectors. Understanding, decision making and sensor control are performed by the human operators, involving high cognitive load. Any automation in the system has traditionally involved bespoke design of centralised systems that are highly specific for the assets/targets/environment under consideration, resulting in complex, non-flexible systems that exhibit poor interoperability. We address a concept of Autonomous Sensor Modules (ASMs) for land ISR, where these modules have the ability to make low-level decisions on their own in order to fulfil a higher-level objective, and plug in, with the minimum of preconfiguration, to a High Level Decision Making Module (HLDMM) through a middleware integration layer. The dual requisites of autonomy and interoperability create challenges around information fusion and asset management in an autonomous hierarchical system, which are addressed in this work. This paper presents the results of a demonstration system, known as Sensing for Asset Protection with Integrated Electronic Networked Technology (SAPIENT), which was shown in realistic base protection scenarios with live sensors and targets. The SAPIENT system performed sensor cueing, intelligent fusion, sensor tasking, target hand-off and compensation for compromised sensors, without human control, and enabled rapid integration of ISR assets at the time of system deployment, rather than at design-time. Potential benefits include rapid interoperability for coalition operations, situation understanding with low operator cognitive burden and autonomous sensor management in heterogenous sensor systems

    The future of camera networks: staying smart in a chaotic world

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    Camera networks become smart when they can interpret video data on board, in order to carry out tasks as a collective, such as target tracking and (re-)identi cation of objects of interest. Unlike today’s deployments, which are mainly restricted to lab settings and highly controlled high-value applications, future smart camera networks will be messy and unpredictable. They will operate on a vast scale, drawing on mobile resources connected in networks structured in complex and changing ways. They will comprise heterogeneous and decentralised aggregations of visual sensors, which will come together in temporary alliances, in unforeseen and rapidly unfolding scenarios. The potential to include and harness citizen-contributed mobile streaming, body-worn video, and robot- mounted cameras, alongside more traditional xed or PTZ cameras, and supported by other non-visual sensors, leads to a number of di cult and important challenges. In this position paper, we discuss a variety of potential uses for such complex smart camera networks, and some of the challenges that arise when staying smart in the presence of such complexity. We present a general discussion on the challenges of heterogeneity, coordination, self-recon gurability, mobility, and collaboration in camera networks

    On-line control of active camera networks

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    Large networks of cameras have been increasingly employed to capture dynamic events for tasks such as surveillance and training. When using active (pan-tilt-zoom) cameras to capture events distributed throughout a large area, human control becomes impractical and unreliable. This has led to the development of automated approaches for on-line camera control. I introduce a new approach that consists of a stochastic performance metric and a constrained optimization method. The metric quantifies the uncertainty in the state of multiple points on each target. It uses state-space methods with stochastic models of the target dynamics and camera measurements. It can account for static and dynamic occlusions, accommodate requirements specific to the algorithm used to process the images, and incorporate other factors that can affect its results. The optimization explores the space of camera configurations over time under constraints associated with the cameras, the predicted target trajectories, and the image processing algorithm. While an exhaustive exploration of this parameter space is intractable, through careful complexity analysis and application domain observations I have identified appropriate alternatives for reducing the space. Specifically, I reduce the spatial dimension of the search by dividing the optimization problem into subproblems, and then optimizing each subproblem independently. I reduce the temporal dimension of the search by using empirically-based heuristics inside each subproblem. The result is a tractable optimization that explores an appropriate subspace of the parameters, while attempting to minimize the risk of excluding the global optimum. The approach can be applied to conventional surveillance tasks (e.g., tracking or face recognition), as well as tasks employing more complex computer vision methods (e.g., markerless motion capture or 3D reconstruction). I present the results of experimental simulations of two such scenarios, using controlled and natural (unconstrained) target motions, employing simulated and real target tracks, in realistic scenes, and with realistic camera networks
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