A particle filter to track multiple objects

Hue, Carine; Cadre, J.-P. Le; Pérez, Patrick

doi:10.1109/mot.2001.937982

Cited by 58 publications

(48 citation statements)

References 9 publications

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“…In this research, each luminaire, within an image frame, is allocated a single feature, which is uncommon. Normally, the object of interest is composed of a number of well-textured features [14,20,27], thus allowing the feature tracking to place in context within the scene. Other approaches match strictly on proximity and similarity [14].…”

Section: Autonomous Feature Trackingmentioning

confidence: 99%

“…However, airport landing lighting comprises some 200 luminaires (for a CAT I ALS) located according to strict standards set by the ICAO [5]. Commaniciu et al [28] and Hue et al [27] assume a non-rigid target, whereas this work uses a rigid target with a dynamic observer. The dynamic observer can in turn introduce vibrations to the acquired image data.…”

Section: Autonomous Feature Trackingmentioning

confidence: 99%

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Fast model-based feature matching technique applied to airport lighting

Niblock

Jia

McMenemy

et al. 2008

IET Sci. Meas. Technol.

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Irwin, G. (2008Abstract: The use of image processing techniques to assess the performance of airport landing lighting using images of it collected from an aircraft-mounted camera is documented. In order to assess the performance of the lighting, it is necessary to uniquely identify each luminaire within an image and then track the luminaires through the entire sequence and store the relevant information for each luminaire, that is, the total number of pixels that each luminaire covers and the total grey level of these pixels. This pixel grey level can then be used for performance assessment. The authors propose a robust model-based (MB) featurematching technique by which the performance is assessed. The development of this matching technique is the key to the automated performance assessment of airport lighting.The MB matching technique utilises projective geometry in addition to accurate template of the 3D model of a landing-lighting system. The template is projected onto the image data and an optimum match found, using nonlinear least-squares optimisation. The MB matching software is compared with standard feature extraction and tracking techniques known within the community, these being the Kanade -Lucus -Tomasi (KLT) and scaleinvariant feature transform (SIFT) techniques. The new MB matching technique compares favourably with the SIFT and KLT feature-tracking alternatives. As such, it provides a solid foundation to achieve the central aim of this research which is to automatically assess the performance of airport lighting.

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Section: Autonomous Feature Trackingmentioning

confidence: 99%

Section: Autonomous Feature Trackingmentioning

confidence: 99%

Fast model-based feature matching technique applied to airport lighting

Niblock

Jia

McMenemy

et al. 2008

IET Sci. Meas. Technol.

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“…An association algorithm was included in [6] or in [17] to assign the observation data set Y t to the correct prior density p(X …”

Section: A Multiple Objects Trackermentioning

confidence: 99%

Robot and obstacles localization and tracking with an external camera ring

Pizarro

Marrón

Peon

et al. 2008

2008 IEEE International Conference on Robotics and Automation

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Abstract-In this paper a ring of calibrated and synchronized cameras is used for achieving robot and obstacle localization inside a common observed area. To avoid complex appearance matching derived from the wide-baseline arrangement of cameras, a metric occupancy grid is obtained by intersection of silhouettes projected onto the floor. A particle filter is proposed for tracking multiple objects by using the grid as observation data. A clustering algorithm is included in the filter to increase the robustness and adaptability of the multimodal estimation task. To preserve identity of the robot from the set of tracked objects, odometry readings are used to compute a Maximum Likelihood (ML) global trajectory identification. As a proof of concept, real results are obtained in a long sequence with a mobile robot moving in a human-cluttered scene.

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“…Similarly, various methods, eg. [3,9,10], use one multi-modal particle distribution for all objects. These essentially try to solve the data association problem of how to assign observations to individual objects and to maintain the modality of the particle distribution when objects interact.…”

Section: Existing Workmentioning

confidence: 99%

“…There are many generalizations of the algorithm to cope with the tracking of multiple similar objects [3,5,6]. These attempt to disambiguate the multiple objects, to allow association between each particular object over multiple frame sequences and to maintain tracking of the full set of objects.…”

Section: Introductionmentioning

confidence: 99%

Using Object Interactions to Improve Particle Filter Performance

Marshall

Mills

Benford

2006

Procedings of the British Machine Vision Conference 2006

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This paper describes and evaluates a novel set of approaches to handle situations where multiple distinct and visually differing objects are tracked, such as tracking of people and objects they are manipulating. Unlike tracking of multiple similar objects, visually different interacting objects can provide an opportunity to improve the tracking accuracy. These approaches are designed for use with Condensation/Particle Filter based algorithms, and allow drop-in replacement of tracker modules for each object type tracked. They use information about the relationships and interactions between objects to improve the tracking, rather than in order to distinguish between the objects, as in current algorithms. They are also designed to be highly efficient, for real time use. The approaches are tested on a challenging set of real data and achieve tracking performance similar to using a single very high dimensional tracker, but with vastly reduced complexity and hence much better time performance.

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A particle filter to track multiple objects

Cited by 58 publications

References 9 publications

Fast model-based feature matching technique applied to airport lighting

Fast model-based feature matching technique applied to airport lighting

Robot and obstacles localization and tracking with an external camera ring

Using Object Interactions to Improve Particle Filter Performance

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