• Title, Summary, Keyword: Particle tracking

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Directional Particle Filter Using Online Threshold Adaptation for Vehicle Tracking

  • Yildirim, Mustafa Eren;Salman, Yucel Batu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.2
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    • pp.710-726
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    • 2018
  • This paper presents an extended particle filter to increase the accuracy and decrease the computation load of vehicle tracking. Particle filter has been the subject of extensive interest in video-based tracking which is capable of solving nonlinear and non-Gaussian problems. However, there still exist problems such as preventing unnecessary particle consumption, reducing the computational burden, and increasing the accuracy. We aim to increase the accuracy without an increase in computation load. In proposed method, we calculate the direction angle of the target vehicle. The angular difference between the direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted, based on their angular difference. Particles with angular difference greater than a threshold is eliminated and the remaining are stored with greater weights in order to increase their probability for state estimation. Threshold value is very critical for performance. Thus, instead of having a constant threshold value, proposed algorithm updates it online. The first advantage of our algorithm is that it prevents the system from failures caused by insufficient amount of particles. Second advantage is to reduce the risk of using unnecessary number of particles in tracking which causes computation load. Proposed algorithm is compared against camshift, direction-based particle filter and condensation algorithms. Results show that the proposed algorithm outperforms the other methods in terms of accuracy, tracking duration and particle consumption.

Development of new integrated particle tracking techniques combining the numerical method, semi-analytical method, and analytical method (수치, 해석적, 준 해석적 및 해석적 방법을 통합한 새로운 입자추적기술 개발)

  • Suk, Hee-Jun
    • Journal of Soil and Groundwater Environment
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    • v.13 no.6
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    • pp.50-61
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    • 2008
  • In this study, new integrated particle tracking algorithm was developed to reduce the inherent problem of Eulerian- Lagrangian method, or adverse effect of particle tracking error on mass balance error. The new integrated particle tracking algorithm includes numerical method, semi-analytical method, and analytical method which consider both temporal and spatial changes of velocity field during time step. Detail of mathematical derivations is well illustrated and four examples are made to verify through the comparison of the new integrated particle tracking with analytical solution or Runge-Kutta method. Additionally, It was shown that the there is better superiority of the new integrated particle tracking algorithm over other existing particle tracking method such as Lu's method.

Multiple Cues Based Particle Filter for Robust Tracking (다중 특징 기반 입자필터를 이용한 강건한 영상객체 추적)

  • Hossain, Kabir;Lee, Chi-Woo
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.552-555
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    • 2012
  • The main goal of this paper is to develop a robust visual tracking algorithm with particle filtering. Visual Tracking with particle filter technique is not easy task due to cluttered environment, illumination changes. To deal with these problems, we develop an efficient observation model for target tracking with particle filter. We develop a robust phase correlation combined with motion information based observation model for particle filter framework. Phase correlation provides straight-forward estimation of rigid translational motion between two images, which is based on the well-known Fourier shift property. Phase correlation has the advantage that it is not affected by any intensity or contrast differences between two images. On the other hand, motion cue is also very well known technique and widely used due to its simplicity. Therefore, we apply the phase correlation integrated with motion information in particle filter framework for robust tracking. In experimental results, we show that tracking with multiple cues based model provides more reliable performance than single cue.

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Visual Object Tracking based on Particle Filters with Multiple Observation (다중 관측 모델을 적용한 입자 필터 기반 물체 추적)

  • Koh, Hyeung-Seong;Jo, Yong-Gun;Kang, Hoon
    • Journal of Korean Institute of Intelligent Systems
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    • v.14 no.5
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    • pp.539-544
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    • 2004
  • We investigate a visual object tracking algorithm based upon particle filters, namely CONDENSATION, in order to combine multiple observation models such as active contours of digitally subtracted image and the particle measurement of object color. The former is applied to matching the contour of the moving target and the latter is used to independently enhance the likelihood of tracking a particular color of the object. Particle filters are more efficient than any other tracking algorithms because the tracking mechanism follows Bayesian inference rule of conditional probability propagation. In the experimental results, it is demonstrated that the suggested contour tracking particle filters prove to be robust in the cluttered environment of robot vision.

Disjoint Particle Filter to Track Multiple Objects in Real-time

  • Chai, YoungJoon;Hong, Hyunki;Kim, TaeYong
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.5
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    • pp.1711-1725
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    • 2014
  • Multi-target tracking is the main purpose of many video surveillance applications. Recently, multi-target tracking based on the particle filter method has achieved robust results by using the data association process. However, this method requires many calculations and it is inadequate for real time applications, because the number of associations exponentially increases with the number of measurements and targets. In this paper, to reduce the computational cost of the data association process, we propose a novel multi-target tracking method that excludes particle samples in the overlapped predictive region between the target to track and marginal targets. Moreover, to resolve the occlusion problem, we define an occlusion mode with the normal dynamic mode. When the targets are occluded, the mode is switched to the occlusion mode and the samples are propagated by Gaussian noise without the sampling process of the particle filter. Experimental results demonstrate the robustness of the proposed multi-target tracking method even in occlusion.

Object Tracking Using Particle Filter with an Improved Observe Method (개선된 Observe 기법을 적용한 Particle Filter 물체 추적)

  • Cho, Hyun-Joong;Lee, Chul-Woo;Jung, Jae-Gi;Kim, Jin-Yul
    • Proceedings of the IEEK Conference
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    • pp.210-212
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    • 2009
  • In object tracking based on the particle filter algorithm controlling the proper distribution of the samples is essential to accurately track the target. If the samples are spread too wide compared to the target size, the tracking accuracy may degrade as some samples can be caught by background clutters that is similar to the target. On the other hands if the samples are spread too narrow, the particle filter may fail to track the abrupt motion of the target. To solve this problem we propose an improved particle filter that adopts "re-weighting" technique at the observe step. We estimate the distribution of the weights of the current samples by its mean and variance. Then the samples are re-weighted so that the appropriate distribution of the samples in proportional to the target scale is obtained at the next select step. The proposed tracking method can avoid convergence to local mean and improve the accuracy of the estimated target state.

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Object Tracking in 3-D Space with Passive Acoustic Sensors using Particle Filter

  • Lee, Jin-Seok;Cho, Shung-Han;Hong, Sang-Jin;Lim, Jae-Chan;Oh, Seong-Jun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.5 no.9
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    • pp.1632-1652
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    • 2011
  • This paper considers the object tracking problem in three dimensional (3-D) space when the azimuth and elevation of the object are available from the passive acoustic sensor. The particle filtering technique can be directly applied to estimate the 3-D object location, but we propose to decompose the 3-D particle filter into the three planes' particle filters, which are individually designed for the 2-D bearings-only tracking problems. 2-D bearing information is derived from the azimuth and elevation of the object to be used for the 2-D particle filter. Two estimates of three planes' particle filters are selected based on the characterization of the acoustic sensor operation in a noisy environment. The Cramer-Rao Lower Bound of the proposed 2-D particle filter-based algorithm is derived and compared against the algorithm that is based on the direct 3-D particle filter.

Specified Object Tracking Problem in an Environment of Multiple Moving Objects

  • Park, Seung-Min;Park, Jun-Heong;Kim, Hyung-Bok;Sim, Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.11 no.2
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    • pp.118-123
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    • 2011
  • Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.

Direction-Based Modified Particle Filter for Vehicle Tracking

  • Yildirim, Mustafa Eren;Ince, Ibrahim Furkan;Salman, Yucel Batu;Song, Jong Kwan;Park, Jang Sik;Yoon, Byung Woo
    • ETRI Journal
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    • v.38 no.2
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    • pp.356-365
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    • 2016
  • This research proposes a modified particle filter to increase the accuracy of vehicle tracking in a noisy and occluded medium. In our proposed method for vehicle tracking, the direction angle of a target vehicle is calculated. The angular difference between the motion direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted depending on their angular distance to the motion direction. Those particles moving in a direction similar to that of the target vehicle are assigned larger weights; this, in turn, increases their probability in a given likelihood function (part of the process of estimation of a target's state parameters). The proposed method is compared against a condensation algorithm. Our results show that the proposed method improves the stability of a particle filter tracker and decreases the particle consumption.

Moving-Target Tracking Based on Particle Filter with TDOA/FDOA Measurements

  • Cho, Jeong-A;Na, Han-Byeul;Kim, Sun-Woo;Ahn, Chun-Soo
    • ETRI Journal
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    • v.34 no.2
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    • pp.260-263
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    • 2012
  • In this letter, we propose a moving-target tracking algorithm based on a particle filter that uses the time difference of arrival (TDOA)/frequency difference of arrival (FDOA) measurements acquired by distributed sensors. It is shown that the performance of the proposed algorithm, based on the particle filter, outperforms the one based on the extended Kalman filter. The use of both the TDOA and FDOA measurements is shown to be effective in the moving-target tracking. It is proven that the particle filter deals with the nonlinear nature of the movingtarget tracking problem successfully.