An algorithm for tracking multiple targets

Reid, Donald B.

doi:10.1109/tac.1979.1102177

Cited by 2,397 publications

(794 citation statements)

References 9 publications

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“…Tracking each occupant, however, has the burden of maintaining state estimates (e.g., position, velocity) for each of them even when, for example, they stop moving to sit at a desk. Nonetheless, it is worthwhile to review some prior literature on this multitarget tracking problem studied for radar and sonar systems [e.g., Bar-Shalom and Tse (1975), Reid (1979), Fortmann et al (1983), Bar-Shalom and Fortmann (1988), BarShalom (1990BarShalom ( , 1992, Blackman and Popoli (1999), Bar-Shalom and Blair (2000)] due to common aspects of the two problems. A sequence of radar or sonar detections associated with a particular object (target) enable estimation of the object's trajectory by techniques such as Kalman filtering (Kalman, 1960) or particle filtering (Gordon et al, 1993).…”

Section: Occupancy Tracking Versus Tracking Occupantsmentioning

confidence: 99%

“…Second, it performs the matching at one instant; thus, it can only be locally optimal at that point in time. Another approach that does account for the time history of detections is the multiple hypothesis test (MHT) (Reid, 1979). At the time of each new detection MHT evaluates a set of hypotheses over the entire set totaling N T detections to date.…”

Section: Occupancy Tracking Versus Tracking Occupantsmentioning

confidence: 99%

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A Framework for Occupancy Tracking in a Building via Structural Dynamics Sensing of Footstep Vibrations

Poston

Buehrer

Tarazaga

2017

Front. Built Environ.

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Counting the number of occupants in building areas over time-occupancy tracking-provides valuable information for responding to emergencies, optimizing thermal conditions or managing personnel. This capability is distinct from tracking individual building occupants as they move within a building, has lower complexity than conventional tracking algorithms require, and avoids privacy concerns that tracking individuals may pose. The approach proposed here is a novel combination of data analytics applied to measurements from a building's structural dynamics sensors (e.g., accelerometers or geophones). Specifically, measurements of footstep-generated structural waves provide evidence of occupancy in a building area. These footstep vibrations can be distinguished from other vibrations, and, once identified, the footsteps can be located. These locations, in turn, form the starting point of estimating occupancy in an area. In order to provide a meaningful occupancy count, however, it is first necessary to associate discrete footsteps with individuals. The proposed framework incorporates a tractable algorithm for this association task. The proposed algorithms operate online, updating occupancy count over time as new footsteps are detected. Experiments with measurements from a public building illustrate the operation of the proposed framework. This approach offers an advantage over others based on conventional technologies by avoiding the cost of a separate sensor system devoted to occupancy tracking.

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Section: Occupancy Tracking Versus Tracking Occupantsmentioning

confidence: 99%

Section: Occupancy Tracking Versus Tracking Occupantsmentioning

confidence: 99%

A Framework for Occupancy Tracking in a Building via Structural Dynamics Sensing of Footstep Vibrations

Poston

Buehrer

Tarazaga

2017

Front. Built Environ.

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“…In the former, a number of the most likely hypotheses are maintained at each time step, where each hypothesis consists of its own set of tracks. This is the original approach, presented in [98], and it is relatively simple to implement. The main drawback, however, is that multiple copies of identical tracks might be maintained in different hypotheses.…”

Section: Multiple Hypothesis Trackermentioning

confidence: 99%

Tracking of Animals Using Airborne Cameras

Veiback¹

2016

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The various elements of a modern target tracking framework are covered. Background theory on pre-processing, modelling and estimation is presented as well as some novel ideas on the topic by the author. In addition, a few applications are posed as target tracking problems for which solutions are gradually constructed as relevant theory is covered.Among considered problems are how to constrain targets to a region, use stateindependent measurements to improve estimation in jump Markov models and how to incorporate observations sampled at an uncertain time into a state-space model.A framework is developed for tracking dolphins constrained to a basin using an overhead camera that suffers from occlusions. In this scenario, conventional motion models would suffer from infeasible predictions outside the basin. A motion model is developed for the dolphins where collisions with nearby walls are avoided by turning. The basin is modelled as a polygon where each point along the edge influences the turn rate of the dolphin. The proposed model results in predictions inside the basin, increasing robustness against occlusions. An extension to a Gaussian mixture background model providing a degree of confidence for detections is used to improve tracking in the presence of shadows. A probabilistic data association filter is also modified to estimate the dolphin extension as an ellipse. The proposed framework is able to maintain tracks through occlusions and poor light conditions. A framework is developed for estimating takeoff times and directions of birds in circular cages using an overhead camera. A jump Markov model is used to model the stationary and flight behaviours of the birds. A proposed extension also incorporates state-independent measurements, such as blurriness, to improve mode estimation. Takeoff times and directions are estimated from mode transitions and results are compared to manually annotated data.The cameras are inaccessible in both applications, disallowing proper calibrations. As an alternative, a method is proposed to estimate stationary camera models from available data and known features in the scene. A map of the basin and the funnel dimensions are used respectively. The method estimates a homography and distortion parameters in an invertible mapping function.An extension to the linear Gaussian state-space models is proposed, incorporating an additional observation with an uncertain timestamp. The posterior distribution of the states is derived for the model, which is shown to be a mixture of Gaussians, as well as some estimators for the distribution. The effects of incorporating the observation with an uncertain timestamp into the model are analysed for a one-dimensional scenario. The model is also applied to improve the GPS position of an orienteering sprinter by using the control position as an observation with an uncertain timestamp. Populärvetenskaplig sammanfattningMålföljning (eng. target tracking) är ett moget forskningsområde med anor tillbaka till åtminstone 30-talet. Då tävlade en ha...

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“…The second approach to dealing with the data association problem called multiple hypothesis tracking (MHT) was proposed by Reid [111]. The idea is to keep multiple possible associations (or hypotheses) of observations to existing tracks, new tracks and possible false alarms.…”

Section: (B) Multi-object Tracking and Data Associationmentioning

confidence: 99%

Distributed inference in wireless sensor networks

Veeravalli

Varshney

2012

Phil. Trans. R. Soc. A.

109

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Statistical inference is a mature research area, but distributed inference problems that arise in the context of modern wireless sensor networks (WSNs) have new and unique features that have revitalized research in this area in recent years. The goal of this paper is to introduce the readers to these novel features and to summarize recent research developments in this area. In particular, results on distributed detection, parameter estimation and tracking in WSNs will be discussed, with a special emphasis on solutions to these inference problems that take into account the communication network connecting the sensors and the resource constraints at the sensors.

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An algorithm for tracking multiple targets

Cited by 2,397 publications

References 9 publications

A Framework for Occupancy Tracking in a Building via Structural Dynamics Sensing of Footstep Vibrations

A Framework for Occupancy Tracking in a Building via Structural Dynamics Sensing of Footstep Vibrations

Tracking of Animals Using Airborne Cameras

Distributed inference in wireless sensor networks

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