An MCMC-based particle filter for multiple person tracking

Zuriarrain, Iker; Lerasle, Frédéric; Arana, Nestor; Devy, Michel

doi:10.1109/icpr.2008.4761045

Cited by 6 publications

(7 citation statements)

References 8 publications

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“…In the recent work, Markov Chain Monte Carlo (MCMC) has been proposed to overcome the computational complexity in PF when the state space increases [25]. While MCMC methods cope better in a high-dimensional state space, a common problem is the need to have a large number of samples, especially when tracking abrupt motion.…”

Section: Related Workmentioning

confidence: 99%

Refined particle swarm intelligence method for abrupt motion tracking

Lim

Chan

Monekosso

et al. 2014

Information Sciences

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Conventional tracking solutions are not feasible in handling abrupt motion as they are based on smooth motion assumption or an accurate motion model. Abrupt motion is not subject to motion continuity and smoothness. To assuage this, we deem tracking as an optimisation problem and propose a novel abrupt motion tracker that based on swarm intelligence -the SwaTrack. Unlike existing swarm-based filtering methods, we first of all introduce an optimised swarm-based sampling strategy to tradeoff between the exploration and exploitation of the search space in search for the optimal proposal distribution. Secondly, we propose Dynamic Acceleration Parameters (DAP) allow on the fly tuning of the best mean and variance of the distribution for sampling. Such innovating idea of combining these strategies in an ingenious way in the PSO framework to handle the abrupt motion, which so far no existing works are found. Experimental results in both quantitative and qualitative had shown the effectiveness of the proposed method in tracking abrupt motions.

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Section: Related Workmentioning

confidence: 99%

Refined particle swarm intelligence method for abrupt motion tracking

Lim

Chan

Monekosso

et al. 2014

Information Sciences

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“…Here, we replace the importance sampling step with MH sampling [12]. The MH sampling drives the particles placed in high probability areas so limit drastically the burst of particle numbers [13]. We will discuss this sampling step in section 4 in more details.…”

Section: Particle Filteringmentioning

confidence: 99%

Visual tracking with singular value particle filter

Luo

Huang

2010

2010 IEEE International Workshop on Machine Learning for Signal Processing

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Robust tracking is an important and challenging problem in computer vision. Most existing algorithms do not work well if there are confusing objects in the surrounding environment or the target appearance has a significant change. This paper describes a novel particle filter for object tracking. First, we treat the blob image of the object as a matrix and adopt singular values to construct the feature model. In the second stage, the particle filter scheme is applied for tracking. According to particle degeneracy Metropolis-Hastings sampling is proposed to obtain more efficient particle filter. Borne out by experiments, we demonstrate the proposed method is able to track well under scale variation and when there are confusing objects in the background. Besides, it has higher performance than conventional particle filters in terms of weight and number of particle.

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“…In Zuriarrain et al (2008), a mixed Markov Chain Monte Carlo/Particle Filtering algorithm was proposed in order to track a variable number of people in an indoors scene. An overview of the algorithm follows, though with implementation details mostly omitted.…”

Section: The Mixed Mcmc/pf Tracking Algorithmmentioning

confidence: 99%

“…In Zuriarrain et al (2008) a hybrid Particle Filtering/Markov Chain Monte Carlo algorithm was proposed that performs the detection and tracking of multiple humans in an indoors environment, which was developed using a PC. In this paper, we take that algorithm as a base and present a design for its imple-mentation in an FPGA based smart camera, following a simulation-based methodology.…”

Section: Introductionmentioning

confidence: 99%

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Implementation Analysis for a Hybrid Particle Filter on an Fpga Based Smart Camera

Zuriarrain

Arana

Lerasle

2010

Proceedings of the International Conference on Computer Vision Theory and Applications

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Design and development of embedded devices which perform computer vision related task presents many challenges, many of which stem from attempting to ?t the complexity of many higher level vision algorithms into the constraints presented by programmable embedded devices. In this paper, we follow a simulationbased methodology in order to develop an architecture which will allow us to implement a mixed Particle Filter/Markov Chain Monte Carlo tracking algorithm in an FPGA-based smart camera, using tools such as SystemC and Transaction LevelModeling (TLM). Use of these tools has allowed us to make some preliminary predictions as to the memory usage and performance of the system, which will be compared to the results of more detailed simulations obtained in the way towards implementing this system.

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An MCMC-based particle filter for multiple person tracking

Cited by 6 publications

References 8 publications

Refined particle swarm intelligence method for abrupt motion tracking

Refined particle swarm intelligence method for abrupt motion tracking

Visual tracking with singular value particle filter

Implementation Analysis for a Hybrid Particle Filter on an Fpga Based Smart Camera

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