On Resampling Algorithms for Particle Filters

Hol, Jeroen D.; Schön, Thomas B.; Gustafsson, Fredrik

doi:10.1109/nsspw.2006.4378824

Cited by 316 publications

(218 citation statements)

References 11 publications

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“…Several resampling techniques have been proposed, see e.g. [6][7][8]. A main scheme for an iteration of the PF algorithm can be summarized as follows.…”

Section: Particle Filtermentioning

confidence: 99%

Robust particle filter by dynamic averaging of multiple noise models

Liu

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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State filtering is a key problem in many signal processing applications. From a series of noisy measurement, one would like to estimate the state of some dynamic system. Existing techniques usually adopt a Gaussian noise assumption which may result in a major degradation in performance when the measurements are with the presence of outliers. A robust algorithm immune to the presence of outliers is desirable. To this end, a robust particle filter (PF) algorithm is proposed, in which the heavier tailed Student's t distributions are employed together with the Gaussian distribution to model the measurement noise. The effect of each model is automatically and dynamically adjusted via a Bayesian model averaging mechanism. The validity of the proposed algorithm is evaluated by illustrative simulations.

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“…Several resampling techniques have been proposed, see e.g. [6][7][8]. A main scheme for an iteration of the PF algorithm can be summarized as follows.…”

Section: Particle Filtermentioning

confidence: 99%

Robust particle filter by dynamic averaging of multiple noise models

Liu

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…For face model update, the AAMT-FR uses the SKL algorithm [13] whose computational complexity is O(dm 2 ), where d and m refer to the dimensionality of the input feature vectors (HOG of the facial captures) and the number of new facial captures considered for face model update, respectively. For tracking, particle filter has been used, whose computational complexity is O(N ), where N is the number of particles re-sampled for a time instance by the filter [54].…”

Section: Time Complexity Analysismentioning

confidence: 99%

Adaptive appearance model tracking for still-to-video face recognition

Dewan

Granger

Marcialis

et al. 2016

Pattern Recognition

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Systems for still-to-video face recognition (FR) seek to detect the presence of target individuals based on reference facial still images or mug-shots. These systems encounter several challenges in video surveillance applications due to variations in capture conditions (e.g., pose, scale, illumination, blur and expression) and to camera inter-operability. Beyond these issues, few reference stills are available during enrollment to design representative facial models of target individuals. Systems for still-to-video FR must therefore rely on adaptation, multiple face representation, or synthetic generation of reference stills to enhance the intra-class variability of face models. Moreover, many FR systems only match high quality faces captured in video, which further reduces the probability of detecting target individuals. Instead of matching faces captured through segmentation to reference stills, this paper exploits Adaptive Appearance Model Tracking (AAMT) to gradually learn a track-face-model for each individual appearing in the scene. The Sequential Karhunen-Loeve technique is used for online learning of these track-face-models within a particle filter-based face tracker. Meanwhile, these models are matched over successive frames against the reference still images of each target individual enrolled to the system, and then matching scores are accumulated over several frames for robust spatiotemporal recognition. A target individual is recognized if scores accumulated for a track-face-model over a fixed time surpass some decision threshold. The main advantage of AAMT over traditional still-to-video FR systems is the greater diversity of facial representation that may be captured during operations, and this can lead to better discrimination for spatiotemporal recognition. Compared to state-of-the-art adaptive biometric systems, the proposed method selects facial captures to update an individual's face model more reliably because it relies on information from tracking. Simulation results obtained with the Chokepoint video dataset indicate that the proposed method provides a significantly higher level of performance compared state-of-the-art systems when a single reference still per individual is available for matching. This higher level of performance is achieved when the diverse facial appearances that are captured in video through AAMT correspond to that of reference stills.

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“…From the literature, four traditional basic resampling algorithms can be identified such as Simple Random Sampling, Stratified Resampling, Residual Resampling (RR), and Systematic Resampling (SR) [9]. All of them can be used in the basic (SISR) particle filter.…”

Section: A Basic Resampling Algorithmsmentioning

confidence: 99%

Research on Resampling Algorithms for Particle Filter

Zeng¹,

Shi²,

Liao³

2014

Proceedings of the 2nd International Conference on Teaching and Computational Science

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Abstract-Particle filter has been widely applied in many fields in recent years. In order to reduce the particle degeneracy problem, some resampling strategies are introduced to improve the real-time performance of particle filtering. In this article, we present many resampling algorithms and their modified strategy, combining with the simulation experiments of two moving-target tracking models and the efficiency of some algorithms are evaluated.

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On Resampling Algorithms for Particle Filters

Cited by 316 publications

References 11 publications

Robust particle filter by dynamic averaging of multiple noise models

Robust particle filter by dynamic averaging of multiple noise models

Adaptive appearance model tracking for still-to-video face recognition

Research on Resampling Algorithms for Particle Filter

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