Entropy distribution and coverage rate-based birth intensity estimation in GM-PHD filter for multi-target visual tracking

Zhou, Xiaolong; Li, Youfu; He, Bingwei

doi:10.1016/j.sigpro.2013.08.002

Cited by 37 publications

(36 citation statements)

References 19 publications

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“…To correctly associate the objects in consecutive frames, an efficient GM-PHD tracker is utilised for object tracking. In the object tracking stage, we have utilised an entropy distribution based method [62] to estimate the birth intensity of the new objects. Moreover, we have handled the partial occlusion caused by hand grasping based on a game theoretical method [63].…”

Section: Object Trackingmentioning

confidence: 99%

How to Build a Supervised Autonomous System for Robot-Enhanced Therapy for Children with Autism Spectrum Disorder

Esteban

Baxter

Belpaeme

et al. 2017

Paladyn, Journal of Behavioral Robotics

114

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Robot-Assisted Therapy (RAT) has successfully been used to improve social skills in children with autism spectrum disorders (ASD) through remote control of the robot in so-called Wizard of Oz (WoZ) paradigms. However, there is a need to increase the autonomy of the robot both to lighten the burden on human therapists (who have to remain in control and, importantly, supervise the robot) and to provide a consistent therapeutic experience. This paper seeks to provide insight into increasing the autonomy level of social robots in therapy to move beyond WoZ. With the final aim of improved human-human social interaction for the children, this multidisciplinary research seeks to facilitate the use of social robots as tools in clinical situations by addressing the challenge of increasing robot autonomy. We introduce the clinical framework in which the developments are tested, alongside initial data obtained from patients in a first phase of the project using a WoZ set-up mimicking the targeted supervised-autonomy behaviour. We further describe the implemented system architecture capable of providing the robot with supervised autonomy.

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Section: Object Trackingmentioning

confidence: 99%

How to Build a Supervised Autonomous System for Robot-Enhanced Therapy for Children with Autism Spectrum Disorder

Esteban

Baxter

Belpaeme

et al. 2017

Paladyn, Journal of Behavioral Robotics

114

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“…A track initiation technique is proposed to detect the position unknown birth targets and is hybridized with PHD and CPHD filter in [142]. In [143], a multitarget visual tracking system that combines object detection with the GM-PHD is developed, in which a new birth intensity estimation method based on entropy distribution and coverage rate is proposed. In [144], the Doppler information (DI) is incorporated for more precise birth target selection and improving the accuracy of predicted states of GM-PHD filtering.…”

Section: ) Target Birth and Spawning Modelmentioning

confidence: 99%

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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Abstract-Multitarget tracking (MTT) in surveillance system is extremely challenging, due to uncertain data association, maneuverable target motion, dense clutter disturbance, and real-time processing requirements. A good many methods have been proposed to cope with these challenges. However, no up-to-date survey is available in the literature that can help to select suitable tracking algorithm for practical problem. This paper provides a comprehensive review of the state-of-the-art motion-based MTT techniques, classifies existing methods into two groups, i.e., the detect-beforetrack (DBT) scheme and the track-before-detect (TBD) scheme. The DBT scheme is employed to achieve robust and tractable tracking performance when the signal-noise-ratio (SNR) is strong. The TBD scheme is used in the scenarios of low SNR, and it aims to cumulate target energy by multiple sensor frames. Furthermore, depending on the data association mechanism, the DBT methods can be classified into two categories, data association based approaches and finite set statistics (FISST) based approaches. And the TBD methods can be classified into non-Bayesian approaches and Bayesian approaches depending on the basis theory used for tracking. For each category, this paper provides the detailed descriptions of the representative algorithms, and examines their pros and cons. Finally, new trends for future research directions are offered.

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“…The parameters for the SMC-PHD are set similar to [9], [10] as: p D = 0.98, p S = 0.99, λ = 0.26 and σ c = 0.1. The uniform density u is (360 × 280) −1 and the number of particles per speaker is ρ = 50.…”

Section: Setup and Performance Metricmentioning

confidence: 99%

“…There are two common ways. The first one is to use a detector as in [8] and [9] where background/foreground (B/F) detection algorithm is run on the frame and the centers of the foreground objects are used as the measurements set. Despite being computationally expensive, this method performs well in tracking of moving objects.…”

Section: Introductionmentioning

confidence: 99%

Audio informed visual speaker tracking with SMC-PHD filter

Kılıç

Barnard

Wang

et al. 2015

2015 IEEE International Conference on Multimedia and Expo (ICME)

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Sequential Monte Carlo probability hypothesis density (SMC-PHD) filter has received much interest in the field of nonlinear non-Gaussian visual tracking due to its ability to handle a variable number of speakers. The SMC-PHD filter employs surviving, spawned and born particles to model the state of the speakers and jointly estimates the variable number of speakers with their states. The born particles play a critical role in the detection of new speakers, which makes it necessary to propagate them in each frame. However, this increases the computational cost of the visual tracker. Here, we propose to use audio data to determine when to propagate the born particles and re-allocate the surviving and spawned particles. In our framework, we employ audio data as an aid to visual SMC-PHD (V-SMC-PHD) filter by using the direction of arrival (DOA) angles of the audio sources to reshape the distribution of the particles. Experimental results on the AV16.3 dataset with multi-speaker sequences show that our proposed audio-visual SMC-PHD (AV-SMC-PHD) filter improves the tracking performance in terms of estimation accuracy and computational efficiency.

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Entropy distribution and coverage rate-based birth intensity estimation in GM-PHD filter for multi-target visual tracking

Cited by 37 publications

References 19 publications

How to Build a Supervised Autonomous System for Robot-Enhanced Therapy for Children with Autism Spectrum Disorder

How to Build a Supervised Autonomous System for Robot-Enhanced Therapy for Children with Autism Spectrum Disorder

A Survey of Motion-Based Multitarget Tracking Methods

Audio informed visual speaker tracking with SMC-PHD filter

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