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151
CITRIC: A low-bandwidth wireless camera network platform
- In Proceedings of the International Conference on Distributed Smart Cameras
, 2008
"... In this paper, we propose and demonstrate a novel wireless camera network system, called CITRIC. The core component of this system is a new hardware platform that integrates a camera, a frequency-scalable (up to 624 MHz) CPU, 16 MB FLASH, and 64 MB RAM onto a single device. The device then connects ..."
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Cited by 52 (11 self)
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In this paper, we propose and demonstrate a novel wireless camera network system, called CITRIC. The core component of this system is a new hardware platform that integrates a camera, a frequency-scalable (up to 624 MHz) CPU, 16 MB FLASH, and 64 MB RAM onto a single device. The device then connects with a standard sensor network mote to form a camera mote. The design enables in-network processing of images to reduce communication requirements, which has traditionally been high in existing camera networks with centralized processing. We also propose a back-end client/server architecture to provide a user interface to the system and support further centralized processing for higher-level applications. Our camera mote enables a wider variety of distributed pattern recognition applications than traditional platforms because it provides more computing power and tighter integration of physical components while still consuming relatively little power. Furthermore, the mote easily integrates with existing low-bandwidth sensor networks because it can communicate over the IEEE 802.15.4 protocol with other sensor network platforms. We demonstrate our system on three applications: image compression, target tracking, and camera localization.
Tracking and coordination of multiple agents using sensor networks: system design, algorithms and experiments
"... This paper considers the problem of pursuit evasion games (PEGs), where a group of pursuers is required to chase and capture a group of evaders in minimum time with the aid of a sensor network. We assume that a sensor network is previously deployed and provides global observability of the surveilla ..."
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Cited by 43 (13 self)
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This paper considers the problem of pursuit evasion games (PEGs), where a group of pursuers is required to chase and capture a group of evaders in minimum time with the aid of a sensor network. We assume that a sensor network is previously deployed and provides global observability of the surveillance region, allowing an optimal pursuit policy. While sensor networks provide global observability, they cannot provide high quality measurements in a timely manner due to packet losses, communication delays, and false detections. This has been the main challenge in developing a real-time control system using sensor networks. We address this challenge by developing a real-time hierarchical control system which decouples the estimation of evader states from the control of pursuers via multiple layers of data fusion. While a sensor network generates noisy, inconsistent, and bursty measurements, the multiple layers of data fusion convert them into consistent and high quality measurements and forward them to the controllers of pursuers in a timely manner. For this control system, three new algorithms are developed: multi-sensor fusion, multi-target tracking and multi-agent coordination algorithms. The multi-sensor fusion algorithm converts correlated sensor measurements into position estimates, the multi-target tracking algorithm tracks an unknown number of targets, and the multi-agent coordination algorithm coordinates pursuers to capture all evaders in minimum time using a robust minimum-time feedback controller. The combined system is evaluated in simulation and tested in a sensor network deployment. To our knowledge, this paper presents the first demonstration of multi-target tracking using a sensor network without relying on classification.
Swarm Coordination for Pursuit Evasion Games Using Sensor Networks
- Proc. Int’l Conf. Robotics and Automation
, 2005
"... Abstract — In this work we consider the problem of pursuit evasion games (PEGs) where a group of pursuers is required to detect, chase and capture a group of evaders with the aid of a sensor network in minimum time. Differently from standards PEGs where the environment and the location of evaders is ..."
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Cited by 41 (5 self)
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Abstract — In this work we consider the problem of pursuit evasion games (PEGs) where a group of pursuers is required to detect, chase and capture a group of evaders with the aid of a sensor network in minimum time. Differently from standards PEGs where the environment and the location of evaders is unknown and a probabilistic map is built based on the pursuer onboard sensors, here we consider a scenario where a sensor network, previously deployed in the region of concern, can detect the presence of moving vehicles and can relay this information to the pursuers. Here we propose a general framework for the design of a hierarchical control architecture that exploit the advantages of a sensor networks by combining both centralized and decentralized real-time control algorithms. We also propose a coordination scheme for the pursuers to minimize the time-to-capture of all evaders. In particular, we focus on PEGs with sensor networks orbiting in space for artificial space debris detection and removal. Index Terms — Sensor networks, pursuit evasion games, vehicle coordination, space vehicles, space debris over the area of interest. This constraint makes designing a cooperative pursuit algorithm harder because lack of complete observability only allows for suboptimal pursuit policies. See Figure 1(left). Furthermore, a smart evaders makes the map-building process dynamic since their location changes over time. The map-learning phase is, by itself, time-consuming and computationally intensive even for simple two-dimensional rectilinear environments [5]. Moreover, inaccurate sensors complicate this process and a probabilistic approach is often required [21]. I.
A hierarchical multiple-target tracking algorithm for sensor networks
- In Proc. of the International Conference on Robotics and Automation
, 2005
"... Abstract — Multiple-target tracking is a canonical application of sensor networks as it exhibits different aspects of sensor networks such as event detection, sensor information fusion, multi-hop communication, sensor management and decision making. The task of tracking multiple objects in a sensor ..."
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Cited by 39 (10 self)
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Abstract — Multiple-target tracking is a canonical application of sensor networks as it exhibits different aspects of sensor networks such as event detection, sensor information fusion, multi-hop communication, sensor management and decision making. The task of tracking multiple objects in a sensor network is challenging due to constraints on a sensor node such as short communication and sensing ranges, a limited amount of memory and limited computational power. In addition, since a sensor network surveillance system needs to operate autonomously without human operators, it requires an autonomous tracking algorithm which can track an unknown number of targets. In this paper, we develop a scalable hierarchical multiple-target tracking algorithm that is autonomous and robust against transmission failures, communication delays and sensor localization error. Index Terms — Sensor networks, multiple-target tracking, Markov chain Monte Carlo, data association
Discrete-Continuous Optimization for Multi-Target Tracking
"... The problem of multi-target tracking is comprised of two distinct, but tightly coupled challenges: (i) the naturally discrete problem of data association, i.e. assigning image observations to the appropriate target; (ii) the naturally continuous problem of trajectory estimation, i.e. recovering the ..."
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Cited by 39 (5 self)
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The problem of multi-target tracking is comprised of two distinct, but tightly coupled challenges: (i) the naturally discrete problem of data association, i.e. assigning image observations to the appropriate target; (ii) the naturally continuous problem of trajectory estimation, i.e. recovering the trajectories of all targets. To go beyond simple greedy solutions for data association, recent approaches often perform multi-target tracking using discrete optimization. This has the disadvantage that trajectories need to be pre-computed or represented discretely, thus limiting accuracy. In this paper we instead formulate multi-target tracking as a discretecontinuous optimization problem that handles each aspect in its natural domain and allows leveraging powerful methods for multi-model fitting. Data association is performed using discrete optimization with label costs, yielding near optimality. Trajectory estimation is posed as a continuous fitting problem with a simple closed-form solution, which is used in turn to update the label costs. We demonstrate the accuracy and robustness of our approach with state-of-theart performance on several standard datasets. 1.
McMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... Abstract—In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequate ..."
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Cited by 36 (1 self)
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Abstract—In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC. Index Terms—Markov chain Monte Carlo, QR factorization, updating, downdating, Rao-Blackwellized, particle filter, multitarget tracking, merged measurements, linear least squares, laser range scanner.
Time synchronization attacks in sensor networks
- in SASN ’05: Proceedings of the 3rd ACM workshop on Security of
, 2005
"... In this chapter, we review time synchronization attacks in wireless sensor networks. We will first consider three of the main time synchronization protocols in sensor network in sections. In section we discuss applications of time synchronization in sensor networks. In section we analyze possible se ..."
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Cited by 35 (3 self)
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In this chapter, we review time synchronization attacks in wireless sensor networks. We will first consider three of the main time synchronization protocols in sensor network in sections. In section we discuss applications of time synchronization in sensor networks. In section we analyze possible security attacks on the existing time synchronization protocols. In section we examine how different sensor network applications are affected by time synchronization attacks. Finally in section we propose possible countermeasures to secure the time synchronization protocols. 1
Fourier Theoretic Probabilistic Inference over Permutations
- JOURNAL OF MACHINE LEARNING RESEARCH
, 2009
"... Permutations are ubiquitous in many real-world problems, such as voting, ranking, and data association. Representing uncertainty over permutations is challenging, since there are n! possibilities, and typical compact and factorized probability distribution representations, such as graphical models, ..."
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Cited by 29 (7 self)
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Permutations are ubiquitous in many real-world problems, such as voting, ranking, and data association. Representing uncertainty over permutations is challenging, since there are n! possibilities, and typical compact and factorized probability distribution representations, such as graphical models, cannot capture the mutual exclusivity constraints associated with permutations. In this paper, we use the “low-frequency” terms of a Fourier decomposition to represent distributions over permutations compactly. We present Kronecker conditioning, a novel approach for maintaining and updating these distributions directly in the Fourier domain, allowing for polynomial time bandlimited approximations. Low order Fourier-based approximations, however, may lead to functions that do not correspond to valid distributions. To address this problem, we present a quadratic program defined directly in the Fourier domain for projecting the approximation onto a relaxation of the polytope of legal marginal distributions. We demonstrate the effectiveness of our approach on a real camera-based multi-person tracking scenario.
General-purpose mcmc inference over relational structures
- In Proceedings of the Proceedings of the Twenty-Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI06
"... Tasks such as record linkage and multi-target tracking, which involve reconstructing the set of objects that underlie some observed data, are particularly challenging for probabilistic inference. Recent work has achieved efficient and accurate inference on such problems using Markov chain Monte Carl ..."
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Cited by 28 (5 self)
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Tasks such as record linkage and multi-target tracking, which involve reconstructing the set of objects that underlie some observed data, are particularly challenging for probabilistic inference. Recent work has achieved efficient and accurate inference on such problems using Markov chain Monte Carlo (MCMC) techniques with customized proposal distributions. Currently, implementing such a system requires coding MCMC state representations and acceptance probability calculations that are specific to a particular application. An alternative approach, which we pursue in this paper, is to use a general-purpose probabilistic modeling language (such as BLOG) and a generic Metropolis-Hastings MCMC algorithm that supports user-supplied proposal distributions. Our algorithm gains flexibility by using MCMC states that are only partial descriptions of possible worlds; we provide conditions under which MCMC over partial worlds yields correct answers to queries. We also show how to use a context-specific Bayes net to identify the factors in the acceptance probability that need to be computed for a given proposed move. Experimental results on a citation matching task show that our general-purpose MCMC engine compares favorably with an application-specific system. 1
Efficient track linking methods for track graphs using network-flow and set-cover techniques
- In CVPR
, 2011
"... This paper proposes novel algorithms that use networkflow and set-cover techniques to perform occlusion reasoning for a large number of small, moving objects in single or multiple views. We designed a track-linking framework for reasoning about short-term and long-term occlusions. We introduce a two ..."
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Cited by 23 (4 self)
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This paper proposes novel algorithms that use networkflow and set-cover techniques to perform occlusion reasoning for a large number of small, moving objects in single or multiple views. We designed a track-linking framework for reasoning about short-term and long-term occlusions. We introduce a two-stage network-flow process to automatically construct a “track graph ” that describes the track merging and splitting events caused by occlusion. To explain short-term occlusions, when local information is sufficient to distinguish objects, the process links trajectory segments through a series of optimal bipartite-graph matches. To resolve long-term occlusions, when global information is needed to characterize objects, the linking process computes a logarithmic approximation solution to the set cover problem. If multiple views are available, our method builds a track graph, independently for each view, and then simultaneously links track segments from each graph, solving a joint set cover problem for which a logarithmic approximation also exists. Through experiments on different datasets, we show that our proposed linear and integer optimization techniques make the track graph a particularly useful tool for tracking large groups of individuals in images. 1.
