<i>Tracking and Data Association</i>

Bar‐Shalom, Yaakov; Fortmann, T.; Cable, Peter G.

doi:10.1121/1.398863

Cited by 2,050 publications

(2,111 citation statements)

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“…A popular approach for tracking speakers with audio and video data is to use a state-space approach based on the Bayesian framework, for example, the Kalman filter (KF) for linear motion and sensor models [10], extensions of KF for the nonlinear models using the first order Taylor expansion including the decentralized Kalman filter (DKF) [11], [12] and extended Kalman filter (EKF) [13], [14], and the particle filter (PF) for nonlinear and non-Gaussian models [15]. In comparison to the KF and EKF approaches, the PF approach is more robust for nonlinear models as it can approach the Bayesian optimal estimate with a sufficiently large number of particles [15].…”

Section: Introductionmentioning

confidence: 99%

Audio Assisted Robust Visual Tracking With Adaptive Particle Filtering

Kılıç

Barnard

Wang

et al. 2015

IEEE Trans. Multimedia

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Abstract-The problem of tracking multiple moving speakers in indoor environments has recently received much attention. Earlier techniques were based purely on vision, but the theoretical and algorithmic advances and a constant growth in speed of processing have led to the emergence of techniques which allow the fusion of audio and visual data. The fusion of multi-modal information has been shown to be instrumental in improving tracking performance, as well as robustness in the case of challenging situations like occlusions (by the limited field of view of cameras or by other speakers). However, data fusion algorithms often suffer from noise corrupting the sensor measurements which cause non-negligible detection errors. Here, a novel approach to combining audio and visual data is proposed. In our framework, we employ audio data as an aid to particle filter (PF) based visual tracking, by using the direction of arrival angles of the audio sources to reshape the typical Gaussian noise distribution of particles in the propagation step and to weight the observation model in the measurement step. This approach is further improved by solving a typical problem associated with the PF. It has been observed that the efficiency and accuracy of the PF usually depend on the number of particles and noise variance used in the estimation and propagation functions for re-allocating these particles at each iteration. Both of these parameters are specified beforehand and are kept fixed in the regular implementation of the PF which makes the tracker unstable in practice. To address these problems, we design an algorithm which adapts both the number of particles and noise variance based on tracking error and the area occupied by the particles in the image. Experiments on the AV 16.3 dataset show the advantage of our proposed methods over the baseline PF method and an existing adaptive PF algorithm for tracking occluded speakers with a significantly reduced number of particles.Index Terms-Audio-visual speaker tracking, particle filter, adaptive particle filter.

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Section: Introductionmentioning

confidence: 99%

Audio Assisted Robust Visual Tracking With Adaptive Particle Filtering

Kılıç

Barnard

Wang

et al. 2015

IEEE Trans. Multimedia

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“…This is relatively simple for single individuals, although false and missed detections become more likely when detection is problematic. Constructing trajectories for multi pie individuals often involves parameterization of a movement model which includes information from previous frames, such as the aocelera lion of each individual or their preferred direction of motion (89,90). Movement models also improve the detection phase of tracking, but ultimately suffer from error propagation and thus can be labor intensive.…”

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confidence: 99%

Automated image-based tracking and its application in ecology

Dell

Bender²,

Branson

et al. 2014

Trends in Ecology & Evolution

454

484

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The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers. Measuring behaviorIndividual behavior (see Glossary) underlies almost all aspects of ecology [1][2][3][4][5]. Accurate and highly resolved behavioral data are therefore critical for obtaining a mechanistic and predictive understanding of ecological systems [5]. Historically, direct observation by trained biologists was used to quantify behavior [6,7]. However, the extent and resolution to which direct observations can be made is highly constrained [8] and the number of individuals that can be observed simultaneously is small. In addition, an exact record of events is not preserved, only the biologist's subjective account of them.Recent technological advances in tracking now make it possible to collect large amounts of highly precise and accurate behavioral data. For many organisms equipment can be attached that provide information about the Glossary Background subtraction: a method used by software to compare the current video frame with a stored picture of the background; any pixel of the current frame that is significantly different from the corresponding pixel in the background is likely to be associated with the body of an animal. Useful in situations where the background is unchanging, for example, when the surface of the background is rigid and lighting does not change. Behavior: the actions of individuals, often in response to stimuli. Behavior can involve movement of the individual's body through space, such as walking or chasing, or can occur while the animal is stationary, such as grooming or eating. Bio-logging: attachment or implantation of equipment to organisms to provide information about their identity, location, behavior, or physiology (e.g., global positioning systems, accelerometers, video cameras, telemetry tags). Ecological interaction: any interaction between an organism and its environment, or between two organisms (i.e., including interactions between conspecifics). Fingerprinting: a method used to identify unmarked individuals using natural variation in their physical and/or behavioral appearance. The method works by transforming the images of each individual ...

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“…Associating measurements with the corresponding object in the world model that generated the measurement can be difficult if visually identical objects appear close to each other, i.e., if the robot faces ambiguities. Data association algorithms are developed to deal with the association problem in a probabilistic manner explicitly taking into account the uncertainty [7]. 3.…”

Section: Appropriate Anchoringmentioning

confidence: 99%